Above are papers related to the following paper: [Paper_ID] Paper #61069. 	[Title] Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1033. 	[Title] The Evolutionary Cost of Learning [Abstract] Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionary time. This paper explores the idea that as well as the benefits to be gained from learning, there may also be costs to be paid for the ability to learn. It is these costs that supply the selection pressure for the genetic assimilation of acquired traits. Two models are presented that attempt to illustrate this assertion. The first uses Kauffman's NK fitness landscapes to show the effect that both explicit and implicit costs have on the assimilation of learnt traits. A characteristic `hump' is observed in the graph of the level of plasticity in the population showing that learning is first selected for and then against as evolution progresses. The second model is a practical example in which neural network controllers are evolved for a small mobile robot. Results from this experiment also show the hump. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129610. 	[Title] Coevolving Communicative Behavior in a Linear Pursuer-Evader Game [Abstract] The pursuer-evader (PE) game is recognized as an important domain in which to study the coevolution of robust adaptive behavior and protean behavior (Miller and Cliff, 1994). Nevertheless, the potential of the game is largely unrealized due to methodological hurdles in coevolutionary simulation raised by PE; versions of the game that have optimal solutions (Isaacs, 1965) are closed-ended, while other formulations are opaque with respect to their solution space, for the lack of a rigorous metric of agent behavior. This inability to characterize behavior, in turn, obfuscates coevolutionary dynamics. We present a new formulation of PE that affords a rigorous measure of agent behavior and system dynamics. The game is moved from the two-dimensional plane to the one-dimensional bit-string; at each time step, the evader generates a bit that the pursuer must simultaneously predict. Because behavior is expressed as a time series, we can employ information theory to provide quantitative analysis of agent activity. Further, this version of PE opens vistas onto the communicative component of pursuit and evasion behavior, providing an open-ended serial communications channel and an open world (via coevolution). Results show that subtle changes to our game determine whether it is open-ended, and profoundly affect the viability of arms-race dynamics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1035. 	[Title] EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY [Abstract] 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent energy environments" (LEE) models to define environments of carefully controlled complexity which allow us to state bounds for random and optimal behaviors that are independent of strategies for achieving the behaviors. Using LEE's analytic basis for defining environments, we then use neural networks (NNets) to model individuals and a steady - state genetic algorithm to model an evolutionary process shaping the NNets, in particular their sensors. Our experiments consider two types of "contact" and "ambient" sensors, and variants where the NNets are not allowed to learn, learn via error correction from internal prediction, and via reinforcement learning. We find that predictive learning, even when using a larger repertoire of the more sophisticated ambient sensors, provides no advantage over NNets unable to learn. However, reinforcement learning using a small number of crude contact sensors does provide a significant advantage. Our analysis of these results points to a tradeoff between the genetic "robustness" of sensors and their informativeness to a learning system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #606479. 	[Title] The Evolution of Communication Schemes Over Continuous Channels [Abstract] Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While others hand implement communication routes and semantics, we explore a method by which communication can evolve. In the experiments described here, we model agents as connectionist networks. We supply each agent with a number of communications channels implemented by the addition of both input and output units for each channel. The output units initiate environmental signals whose amplitude decay over distance and are perturbed by environmental noise. An agent does not receive input from other individuals, rather the agents input reects the summation of all other agents output signals along that channel. Because we use real-valued activations, the agents communicate using real-valued vectors. Under our evolutionary program, GNARL, the agents coevolve a communication scheme over continuous channels which conveys task-spe cific information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101263. 	[Title] Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries [Abstract] We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against finite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current action is determined by some simple boolean formula on the recent history of play), and games against statistical adversaries (whose current action is determined by some simple function of the statistics of the entire history of play). In both cases we give efficient algorithms for learning to play penny-matching and a more difficult game called contract . We also give the most powerful positive result to date for learning to play against finite automata, an efficient algorithm for learning to play any game against any finite automata with probabilistic actions and low cover time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108047. 	[Title] Evolving a Team [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61073. 	[Title] Evolution of Homing Navigation in a Real Mobile Robot [Abstract] In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153942. 	[Title]  [Abstract] As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in [Koza and Andre 1995] is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach [Tufts 1995], although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach. The idea of simulating a MIMD machine using a SIMD architecture is not new [Hillis and Steele 1986; Littman and Metcalf 1990; Dietz and Cohen 1992]. One of the original ideas for the Connection Machine [Hillis and Steele 1986] was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a universal Turing machine (TM). With different turing machine specifications stored in each local memory, each processor would simply have its own tape, tape head, state table and state pointer, and the simulation would be performed by repeating the basic TM operations simultaneously. Of course, such a simulation would be very inefficient, and difficult to program, but would have the advantage of being really MIMD, where no SIMD processor would be in idle state, until its simulated machine halts. Now let us consider an alternative idea, that each SIMD processor would simulate an individual stored program computer using a simple instruction set. For each step of the simulation, the SIMD system would sequentially execute each possible instruction on the subset of processors whose next instruction match it. For a typical assembly language, even with a reduced instruction set, most processors would be idle most of the time. However, if the set of instructions implemented on the virtual processor is very small, this approach can be fruitful. In the case of Genetic Programming, the "instruction set" is composed of the specified set of functions designed for the task. We will show below that with a precompilation step, simply adding a push, a conditional, and unconditional branching and a stop instruction, we can get a very effective MIMD simulation running. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153816. 	[Title]  [Abstract] Co-evolution of Pursuit and Evasion II: Simulation Methods and Results fl Abstract In a previous SAB paper [10], we presented the scientific rationale for simulating the coevolution of pursuit and evasion strategies. Here, we present an overview of our simulation methods and some results. Our most notable results are as follows. First, co-evolution works to produce good pursuers and good evaders through a pure bootstrapping process, but both types are rather specially adapted to their opponents' current counter-strategies. Second, eyes and brains can also co-evolve within each simulated species for example, pursuers usually evolved eyes on the front of their bodies (like cheetahs), while evaders usually evolved eyes pointing sideways or even backwards (like gazelles). Third, both kinds of coevolution are promoted by allowing spatially distributed populations, gene duplication, and an explicitly spatial morphogenesis program for eyes and brains that allows bilateral symmetry. The paper concludes by discussing some possible applications of simulated pursuit-evasion coevolu tion in biology and entertainment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #568857. 	[Title] Evolving Self-Supporting Structures Page 18 References Evolution of Visual Control Systems for Robots. To appear [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57119. 	[Title] New Methods for Competitive Coevolution [Abstract] We consider "competitive coevolution," in which fitness is based on direct competition among individuals selected from two independently evolving populations of "hosts" and "parasites." Competitive coevolution can lead to an "arms race," in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. "Competitive fitness sharing" changes the way fitness is measured, "shared sampling" provides a method for selecting a strong, diverse set of parasites, and the "hall of fame" encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods, and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593060. 	[Title] 17 Massively Parallel Genetic Programming [Abstract] As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in the chapter by Koza and Andre ([11]) is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach ([20]), although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach. The idea of simulating a MIMD machine using a SIMD architecture is not new ([8, 15]). One of the original ideas for the Connection Machine ([8]) was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a universal Turing machine (TM). With different turing machine specifications stored in each local memory, each processor would simply have its own tape, tape head, state table and state pointer, and the simulation would be performed by repeating the basic TM operations simultaneously. Of course, such a simulation would be very inefficient, and difficult to program, but would have the advantage of being really MIMD, where no SIMD processor would be in idle state, until its simulated machine halts. Now let us consider an alternative idea, that each SIMD processor would simulate an individual stored program computer using a simple instruction set. For each step of the simulation, the SIMD system would sequentially execute each possible instruction on the subset of processors whose next instruction match it. For a typical assembly language, even with a reduced instruction set, most processors would be idle most of the time. However, if the set of instructions implemented on the virtual processor is very small, this approach can be fruitful. In the case of Genetic Programming, the "instruction set" is composed of the specified set of functions designed for the task. We will show below that with a precompilation step, simply adding a push, a conditional, and unconditional branching and a stop instruction, we can get a very effective MIMD simulation running. This chapter reports such an implementation of GP on a MasPar MP-2 parallel computer. The configuration of our system is composed of 4K processor elements Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128997. 	[Title] Stochastic Random or probabilistic but with some direction. For example the arrival of people at [Abstract] Simulated Annealing Search technique where a single trial solution is modified at random. An energy is defined which represents how good the solution is. The goal is to find the best solution by minimising the energy. Changes which lead to a lower energy are always accepted; an increase is probabilistically accepted. The probability is given by exp(E=k B T ). Where E is the change in energy, k B is a constant and T is the Temperature. Initially the temperature is high corresponding to a liquid or molten state where large changes are possible and it is progressively reduced using a cooling schedule so allowing smaller changes until the system solidifies at a low energy solution. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45605. 	[Title] Learning and evolution in neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44455. 	[Title] Evolving Visually Guided Robots [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28456. 	[Title] A Competitive Approach to Game Learning [Abstract] Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm makes repeated use of a strategy learning component that can learn strategies which defeat a given set of opponents. We describe game learning in terms of sets H and X of first and second player strategies, and connect the model with more familiar models of concept learning. We show the importance of the ideas of teaching set [20] and specification number [19] k in this new context. The performance of several competitive algorithms is investigated, using both worst-case and randomized strategy learning algorithms. Our central result (Theorem 4) is a competitive algorithm that solves games in a total number of strategies polynomial in lg(jHj), lg(jX j), and k. Its use is demonstrated, including an application in concept learning with a new kind of counterexample oracle. We conclude with a complexity analysis of game learning, and list a number of new questions arising from this work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8874. 	[Title] A Cooperative Coevolutionary Approach to Function Optimization [Abstract] A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #210871. 	[Title] Coevolving High-Level Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #606647. 	[Title] Dynamics of Co-evolutionary Learning [Abstract] Co-evolutionary learning, which involves the embedding of adaptive learning agents in a fitness environment which dynamically responds to their progress, is a potential solution for many technological chicken and egg problems, and is at the heart of several recent and surprising successes, such as Sim's artificial robot and Tesauro's backgammon player. We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [Koza, 1992]. Instead of using absolute fitness, we use a relative fitness [Angeline & Pollack, 1993] based on a competition for coverage of the data set. As the population reproduces, the fitness function driving the selection changes, and subproblem niches are opened, rather than crowded out. The solutions found by our method have a symbiotic structure which suggests that by holding niches open, crossover is better able to discover modular build ing blocks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #210872. 	[Title] An Evolutionary Algorithm that Constructs Recurrent Neural Networks [Abstract] Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure and function are not well understood. Evolutionary computation, which includes genetic algorithms and evolutionary programming, is a population-based search method that has shown promise in such complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. This algorithms empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38205. 	[Title] Competitive Environments Evolve Better Solutions for Complex Tasks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197054. 	[Title] Evolving Obstacle Avoidance Behavior in a Robot Arm [Abstract] Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input/output examples are necessary, since neuro-evolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #117316. 	[Title] A Simulation of Adaptive Agents in a Hostile Environment [Abstract] In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17476. 	[Title] Markov games as a framework for multi-agent reinforcement learning [Abstract] In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #182094. 	[Title] HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592975. 	[Title] Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP [Abstract] This paper presents Limited Error Fitness (LEF), a modification to the standard supervised learning approach in Genetic Programming (GP), in which an individual's fitness score is based on how many cases remain uncovered in the ordered training set after the individual exceeds an error limit. The training set order and the error limit are both altered dynamically in response to the performance of the fittest individual in the previous generation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #975567. 	[Title] Adaptive Behavior in Competing Co-Evolving Species [Abstract] Co-evolution of competitive species provides an interesting testbed to study the role of adaptive behavior because it provides unpredictable and dynamic environments. In this paper we experimentally investigate some arguments for the co-evolution of different adaptive protean behaviors in competing species of predators and preys. Both species are implemented as simulated mobile robots (Kheperas) with infrared proximity sensors, but the predator has an additional vision module whereas the prey has a maximum speed set to twice that of the predator. Different types of variability during life for neurocontrollers with the same architecture and genetic length are compared. It is shown that simple forms of pro-teanism affect co-evolutionary dynamics and that preys rather exploit noisy controllers to generate random trajectories, whereas predators benefit from directional-change controllers to improve pursuit behavior. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144212. 	[Title] Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects [Abstract] Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a nontrivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592986. 	[Title] Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming [Abstract] This paper looks at the use of small populations in Genetic Programming (GP), where the trend in the literature appears to be towards using as large a population as possible, which requires more memory resources and CPU-usage is less efficient. Dynamic Subset Selection (DSS) and Limited Error Fitness (LEF) are two different, adaptive variations of the standard supervised learning method used in GP. This paper compares the performance of GP, GP+DSS, and GP+LEF, on a 958 case classification problem, using a small population size of 50. A similar comparison between GP and GP+DSS is done on a larger and messier 3772 case classification problem. For both problems, GP+DSS with the small population size consistently produces a better answer using fewer tree evaluations than other runs using much larger populations. Even standard GP can be seen to perform well with the much smaller population size, indicating that it is certainly worth an exploratory run or three with a small population size before assuming that a large population size is necessary. It is an interesting notion that smaller can mean faster and better. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190697. 	[Title] Analysis of Neurocontrollers Designed by Simulated Evolution [Abstract] Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #595056. 	[Title] Using Genetic Programming to Evolve Board Evaluation Functions [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190706. 	[Title] Selection for Wandering Behavior in a Small Robot [Abstract] We have evolved artificial neural networks to control the wandering behavior of small robots. The task was to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in small Lego (Trademark) robot, controlled by a Motorola (Trademark) 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program control; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110515. 	[Title] Environmental Effects on Minimal Behaviors in the Minimat World [Abstract] The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #41714. 	[Title] Issues in Evolutionary Robotics [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107062. 	[Title] Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution. [Abstract] Individual lifetime learning can `guide' an evolving population to areas of high fitness in genotype space through an evolutionary phenomenon known as the Baldwin effect (Baldwin, 1896; Hin-ton & Nowlan, 1987). It is the accepted wisdom that this guiding speeds up the rate of evolution. By highlighting another interaction between learning and evolution, that will be termed the Hiding effect, it will be argued here that this depends on the measure of evolutionary speed one adopts. The Hiding effect shows that learning can reduce the selection pressure between individuals by `hiding' their genetic differences. There is thus a trade-off between the Baldwin effect and the Hiding effect to determine learning's influence on evolution and two factors that contribute to this trade-off, the cost of learning and landscape epis tasis, are investigated experimentally. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594047. 	[Title] A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks [Abstract] Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches. As we can observe, however, not only the controller but also the robot body itself can affect the behavior of the robot in a robot system. In this paper, we develop a hybrid GP/GA approach to evolve both controllers and robot bodies to achieve behavior-specified tasks. In order to assess the performance of the developed approach, it is used to evolve a simulated agent, with its own controller and body, to do obstacle avoidance in the simulated environment. Experimental results show the promise of this work. In addition, the importance of co-evolving controllers and robot bodies is analyzed and discussed in this paper. Please summarise all the given information.

Paper #61069 has the title `Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European
` and the abstract `nan`.
Question: Which category should Paper #61069 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #61069 has the title `Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European
` and the abstract `nan`.
Question: Which category should Paper #61069 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms