Above are papers related to the following paper: [Paper_ID] Paper #56115. 	[Title] Improving Tactical Plans with Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28290. 	[Title] Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task [Abstract] difficult. We face this problem using an architecture based on learning classifier systems and on the description of the learning technique used and of the organizational structure proposed, we present experiments that show how behaviour acquisition can be achieved. Our simulated robot learns to structural properties of animal behavioural organization, as proposed by ethologists. After a Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83461. 	[Title] Dlab: A Declarative Language Bias Formalism [Abstract] We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of Dlab is available by ftp access. Keywords: declarative language bias, concept learning, knowledge dis covery Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58758. 	[Title] A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms [Abstract] In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83725. 	[Title] Explanations of Empirically Derived Reactive Plans [Abstract] Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as genetic algorithms can develop high performance reactive rules for interesting sequential decision tasks. We have previously described an implemented system, called SAMUEL, for learning reactive plans and have shown that the system can successfully learn rules for a laboratory scale tactical problem. In this paper, we describe a method for deriving explanations to justify the success of such empirically derived rule sets. The method consists of inferring plausible subgoals and then explaining how the reactive rules trigger a sequence of actions (i.e., a stra tegy) to satisfy the subgoals. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17811. 	[Title] #1 Robust Feature Selection Algorithms [Abstract] Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. This paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1688. 	[Title] Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems [Abstract] Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12576. 	[Title] Learning Sequential Decision Rules Using Simulation Models and Competition [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #206371. 	[Title] Genetic Algorithms for Adaptive Planning of Path and Trajectory of a Mobile Robot in 2D Terrains [Abstract] This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69284. 	[Title] An Overview of Genetic Algorithms Part 1, Fundamentals [Abstract]  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 #1108267. 	[Title] Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks [Abstract] We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69296. 	[Title] A Case Study on Tuning of Genetic Algorithms by Using Performance Evaluation Based on Experimental Design [Abstract] This paper proposes four performance measures of a genetic algorithm (GA) which enable us to compare different GAs for an op timization problem and different choices of their parameters' values. The performance measures are defined in terms of observations in simulation, such as the frequency of optimal solutions, fitness values, the frequency of evolution leaps, and the number of generations needed to reach an optimal solution. We present a case study in which parameters of a GA for robot path planning was tuned and its performance was optimized through performance evaluation by using the measures. Especially, one of the performance measures is used to demonstrate the adaptivity of the GA for robot path planning. We also propose a process of systematic tuning based on techniques for the design of experiments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56112. 	[Title] Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. Specifically, either the model or the target environment may contain noise. These experiments examine the effect of learning tactical plans without noise and then testing the plans in a noisy environment, and the effect of learning plans in a noisy simulator and then testing the plans in a noise-free environment. Empirical results show that, while best result are obtained when the training model closely matches the target environment, using a training environment that is more noisy than the target environment is better than using using a training environment that has less noise than the target environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56119. 	[Title] Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation [Abstract] Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10169. 	[Title] Learning Boolean Concepts in the Presence of Many Irrelevant Features [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #127033. 	[Title] LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS [Abstract] Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may be costly or dangerous. In addition, time constraints might limit the number of experiences during learning in the real world, while in many cases, the simulation model can be made to run faster than real time. Since learning may require experimenting with behaviors that might occasionally produce unacceptable results if applied to the real world, or might require too much time in the real environment, we assume that hypothetical behaviors will be evaluated in a simulation model (the off-line system). As illustrated in Figure 1, the current best behavior can be placed in the real, on-line system, while learning continues in the off-line system [1]. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. The expectation is that behaviors learned in these simulations will be useful in real-world environments. Previous studies have illustrated that knowledge learned under simulation is robust and might be applicable to the real world if the simulation is more general (i.e. has more noise, more varied conditions, etc.) than the real world environment [2]. Where this is not possible, it is important to identify the differences between the simulation and the world and note the effect upon the learning process. The research reported here continues to examine this hypothesis. The next section very briefly explains the learning algorithm (and gives pointers to where more extensive documentation can be found). After that, the actual robot is described. Then we describe the simulation of the robot. The task _______________ actual robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #509379. 	[Title] Model selection using measure functions [Abstract] The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of selecting and evaluating learned models (classifiers). In addition, it makes it possible to state a learning problem as a computational problem. The the known prior (meta-)knowledge about the problem domain is captured in a measure function that, to each possible combination of a training set and a classifier, assigns a value describing how good the classifier is. The computational problem is then to find a classifier maximizing the measure function. We argue that measure functions are of great value for practical applications. Besides of being a tool for model selection, they: (i) force us to make explicit the relevant prior knowledge about the learning problem at hand, (ii) provide a deeper understanding of existing algorithms, and (iii) help us in the construction of problem-specific algorithms. We illustrate the last point by suggesting a novel algorithm based on incremental search for a classifier that optimizes a given measure function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105221. 	[Title] ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES [Abstract] Autonomous vehicles are likely to require sophisticated software controllers to maintain vehicle performance in the presence of vehicle faults. The test and evaluation of complex software controllers is expected to be a challenging task. The goal of this e ffort is to apply machine learning techniques from the field of arti ficial intelligence to the general problem of evaluating an intelligent controller for an autonomous vehicle. The approach involves subjecting a controller to an adaptively chosen set of fault scenarios within a vehicle simulator, and searching for combinations of faults that produce noteworthy performance by the vehicle controller. The search employs a genetic algorithm. We illustrate the approach by evaluating the performance of a subsumption-based controller for an autonomous vehicle. The preliminary evidence suggests that this approach is an e ffective alternative to manual testing of sophisticated software controllers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #416455. 	[Title] ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS [Abstract] This paper reports on recent results using genetic algorithms to learn decision rules for complex robot behaviors. The method involves evaluating hypothetical rule sets on a simulator and applying simulated evolution to evolve more effective rules. The main contributions of this paper are (1) the task learned is a complex behavior involving multiple mobile robots, and (2) the learned rules are verified through experiments on operational mobile robots. The case study involves a shepherding task in which one mobile robot attempts to guide another robot to a specified area. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144330. 	[Title] Utilizing Prior Concepts for Learning [Abstract] The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96845. 	[Title] Evolution-based Discovery of Hierarchical Behaviors [Abstract] Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate modularization. In this paper we compare several randomized algorithms for learning modular procedural representations. The main algorithm, called Adaptive Representation through Learning (ARL) is a genetic programming extension that relies on the discovery of subroutines. ARL is suitable for learning hierarchies of subroutines and for constructing policies to complex tasks. ARL was successfully tested on a typical reinforcement learning problem of controlling an agent in a dynamic and nondeterministic environment where the discovered subroutines correspond to agent behaviors. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6741. 	[Title] LEARNING CONCEPTS BY ASKING QUESTIONS [Abstract] Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63832. 	[Title] An Evolutionary Approach to Learning in Robots [Abstract] Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135130. 	[Title] USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES [Abstract] Truly autonomous vehicles will require both projec - tive planning and reactive components in order to perform robustly. Projective components are needed for long-term planning and replanning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real-time, and themselves can exhibit robust behavior, but lack the ability to expli - citly reason about future states over a long time period. This work addresses the problem of creating reactive components for autonomous vehicles. Creating reactive behaviors (stimulus-response rules) is generally difficult, requiring the acquisition of much knowledge from domain experts, a problem referred to as the knowledge acquisition bottleneck. SAMUEL is a system that learns reactive behaviors for autonomous agents. SAMUEL learns these behaviors under simulation, automating the process of creating stimulus-response rules and therefore reducing the bottleneck. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. Current work is investigating how well behaviors learned under simulation environments work in real world environments. In this paper, we describe SAMUEL, and describe behaviors that have been learned for simulated autonomous aircraft, autonomous underwater vehicles, and robots. These behaviors include dog fighting, missile evasion, track - ing, navigation, and obstacle avoidance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107674. 	[Title] EVOLUTIONARY ALGORITHMS IN ROBOTICS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129573. 	[Title] The Exploitation of Cooperation in Iterated Prisoner's Dilemma [Abstract] We follow Axelrod [2] in using the genetic algorithm to play Iterated Prisoner's Dilemma. Each member of the population (i.e., each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies, causing an "arms race" of innovation [3]. We conduct two sets of experiments. The first set investigates what conditions evolve the best strategies. The second set studies the robustness of the strategies thus evolved, that is, are the strategies useful only in the round robin of its population or are they effective against a wide variety of opponents? Our results indicate that the population has nearly always converged by about 250 generations, by which time the bias in the population has almost always stabilised at 85%. Our results confirm that cooperation almost always becomes the dominant strategy [1, 2]. We can also confirm that seeding the population with expert strategies is best done in small amounts so as to leave the initial population with plenty of genetic diversity [7]. The lack of robustness in strategies produced in the round robin evaluation is demonstrated by some examples of a population of nave cooperators being exploited by a defect-first strategy. This causes a sudden but ephemeral decline in the population's average score, but it recovers when less nave cooperators emerge and do well against the exploiting strategies. This example of runaway evolution is brought back to reality by a suitable mutation, reminiscent of punctuated equilibria [12]. We find that a way to reduce such navity is to make the GA population play against an extra, Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104999. 	[Title] EVOLVING ROBOT BEHAVIORS [Abstract] This paper discusses the use of evolutionary computation to evolve behaviors that exhibit emergent intelligent behavior. Genetic algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. Some of the emergent behavior is described in detail. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #574710. 	[Title] ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS [Abstract] This work explores the use of machine learning methods for extracting knowledge from simulations of complex systems. In particular, we use genetic algorithms to learn rule-based strategies used by autonomous robots. The evaluation of a given strategy may require several executions of a simulation to produce a meaningful estimate of the quality of the strategy. As a consequence, the evaluation of a single individual in the genetic algorithm requires a fairly substantial amount of computation. Such a system suggests the sort of large-grained parallelism that is available on a network of workstations. We describe an implementation of a parallel genetic algorithm, and present case studies of the resulting speedup on two robot learning tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62718. 	[Title] AN ENHANCER FOR REACTIVE PLANS [Abstract] This paper describes our method for improving the comprehensibility, accuracy, and generality of reactive plans. A reactive plan is a set of reactive rules. Our method involves two phases: (1) formulate explanations of execution traces, and then (2) generate new reactive rules from the explanations. Since the explanation phase has been previously described, the primary focus of this paper is the rule generation phase. This latter phase consists of taking a subset of the explanations and using these explanations to generate a set of new reactive rules to add to the original set. The particular subset of the explanations that is chosen yields rules that provide new domain knowledge for handling knowledge gaps in the original rule set. The original rule set, in a complimentary manner, provides expertise to fill the gaps where the domain knowledge provided by the new rules is incomplete. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46079. 	[Title] A Sequential Niche Technique for Multimodal Function Optimization [Abstract] c fl UWCC COMMA Technical Report No. 93001, February 1993 x No part of this article may be reproduced for commercial purposes. Abstract A technique is described which allows unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. We describe an algorithm based on a traditional genetic algorithm (GA). This involves iterating the GA, but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This is achieved by applying a fitness derating function to the raw fitness function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GA, or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The technique is at least as fast as fitness sharing methods. It provides a speedup of between 1 and 10p on a problem with p optima, depending on the value of p and the convergence time complexity. Please summarise all the given information.

Paper #56115 has the title `Improving Tactical Plans with Genetic Algorithms  
` and the abstract ` 
`.
Question: Which category should Paper #56115 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #56115 has the title `Improving Tactical Plans with Genetic Algorithms  
` and the abstract ` 
`.
Question: Which category should Paper #56115 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