nils-herrmann/clean_scirepeval huggingface dataset:
Evolution of a Low-Distortion, Low-Bias 60 Decibel Op Amp with Good Frequency Generalization using Genetic Programming

ABSTRACTGenetic programming was used toevolve both the topology and thesizing (numerical values) for eachcomponent of a low-distortion, low-bias 60 decibel (1000-to-1) amplifiercircuit with good frequencygeneralization. The evolved circuitwas composed of two types oftransistors (active elements) as well asresistors and capacitors.  The problem of circuit synthesis involves designing anelectrical circuit that satisfies user-specified design goals. Acomplete design of an electrical circuit includes both itstopology and the sizing of all its components. Thetopology of a circuit consists of the number of componentsin the circuit, the type of each component, and a list of allthe connections between the components. The sizing of acircuit consists of the component value(s) of eachcomponent.Evolvable hardware is one approach to automated circuitsynthesis. Some of the early pioneering work in this fieldincludes that of Higuchi, Niwa, Tanaka, Iba, de Garis, andFuruya (1993), Hemmi, Mizoguchi, and Shimohara (1994);Mizoguchi, Hemmi, and Shimohara (1994); and the workpresented at the 1995 workshop on evolvable hardware inLausanne (Sanchez and Tomassini 1996).The design of analog circuits and mixed analog-digitalcircuits has not proved to be amenable to automation(Rutenbar 1993). In DARWIN (Kruiskamp and Leenaerts1995), a CMOS op amp circuit was evolved using a geneticalgorithm. However, the topology of each op amp was oneof 24 hand-designed topologies. Thompson (1996) used agenetic algorithm to evolve a frequency discriminator on aXilinx 6216 reconfigurable processor in analog mode.
The Design of Analog Circuits by Means of Genetic Programming

John R. Koza Section on Medical Informatics Department of Medicine School of Medicine Stanford University Stanford, California 94305 koza@smi.stanford.edu Forrest H Bennett III Chief Scientist Genetic Programming Inc. Los Altos, California 94023 forrest@evolute.com David Andre Division of Computer Science University of California Berkeley, California 94720 dandre@cs.berkeley.edu Martin A. Keane Chief Scientist Econometrics Inc. 111 E. Wacker Dr. Chicago, Illinois 60601 makeane@ix.netcom.com
Evolution of Intricate Long-Distance Communication Signals in Cellular Automata Using Genetic Programming

A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326%. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it utilizes a finegrained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space.
State abstraction for programmable reinforcement learning agents

Safe state abstraction in reinforcement learning allows an agent to ignore aspects of its current state that are irrelevant to its current decision, and therefore speeds up dynamic programming and learning. This paper explores safe state abstraction in hierarchical reinforcement learning, where learned behaviors must conform to a given partial, hierarchical program. Unlike previous approaches to this problem, our methods yield significant state abstraction while maintaining <i>hierarchical optimality</i>, i.e., optimality among all policies consistent with the partial program. We show how to achieve this for a partial programming language that is essentially Lisp augmented with nondeterministic constructs. We demonstrate our methods on two variants of Dietterich's taxi domain, showing how state abstraction and hierarchical optimality result in faster learning of better policies and enable the transfer of learned skills from one problem to another.
Genetic Programming: Biologically Inspired Computation That Creatively Solves Non-trivial Problems

This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. Starting with a primordial ooze of thousands of randomly created computer programs, genetic programming progressively breeds a population of computer programs over a series of generations using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology. The technique is illustrated by its application to a non-trivial problem involving the automatic synthesis (design) of a lowpass filter circuit. The evolved results are competitive with human-produced solutions to the problem. In fact, four of the automatically created circuits exhibit human-level creativity and inventiveness, as evidenced by the fact that they correspond to four inventions that were patented between 1917 and 1936.
Programmable Reinforcement Learning Agents

This dissertation examines the use of partial programming as a means of designing agents for large Markov Decision Problems. In this approach, a programmer specifies only that which they know to be correct and the system then learns the rest from experience using reinforcement learning. 
In contrast to previous low-level languages for partial programming, this dissertation presents ALisp, a Lisp-based high-level partial programming language. ALisp allows the programmer to constrain the policies considered by a learning process and to express his or her prior knowledge in a concise manner. Optimally completing a partial ALisp program is shown to be equivalent to solving a Semi-Markov Decision Problem (SMDP). Under a finite memory-use condition, online learning algorithms for ALisp are proved to converge to an optimal solution of the SMDP and thus to an optimal completion of the partial program. 
This dissertation then presents methods for exploiting the modularity allows an agent to ignore aspects of its current state that are irrelevant to its current decision, and therefore speeds up reinforcement learning. By decomposing representations of the value of actions along subroutine boundaries, optimality, i.e., optimality among all policies consistent with the partial program. These methods are demonstrated on two simulated taxi tasks. 
Function approximation, a method for representing the value of actions, allows reinforcement learning to be applied to problems where exact methods are intractable. Soft shaping is a method for guiding an agent toward a solution without constraining the search space. Both can be integrated with ALisp. ALisp with function approximation and reward shaping is successfully applied on a difficult continuous variant of the simulated taxi task. 
Together, the methods presented in this work comprise a system for agent design that allows the programmer to specify what they know, hint at what they suspect using soft shaping, and leave unspecified that which they don't know; the system then optimally completes the program through experience and takes advantage of the hierarchical structure of the specified program to speed learning.
Practical reinforcement learning in continuous domains

Many real-world domains have continuous features and actions, whereas the majority of results in the reinforcement learning community are for finite Markov decision processes. Much of the work that addresses continuous domains either uses discretization or simple parametric function approximators. A drawback to some commonly-used parametric function approximation techniques, such as neural networks, is that parametric methods can "forget" and concentrate representational power on new examples. In this paper, we propose a practical architecture for model-based reinforcement learning in continuous state and action spaces that avoids the above difficulties by using an instance-based modeling technique. We present a method for learning and maintaining a value function estimate using instance-based learners, and show that our method compares favorably to other function approximation methods, such as neural networks. Furthermore, our reinforcement learning algorithm learns an explicit model of the environment simultaneously with a value function and policy. The use of a model is beneficial, first, because it allows the agent to make better use of its experiences through simulated planning steps. Second, the use of a model makes it straightforward to provide prior information to the system in the form of the structure of the environmental model. We extend a technique called generalized prioritized sweeping to the continuous case in order to focus the agent''s planning steps on those states where the current value is most likely to be incorrect. We illustrate our algorithm''s effectiveness with results on several control domains.
Genetic programming III: Darwinian invention and problem solving [Book Review]

One of the major challenges in the field of integrated circuit design is coping with difficult combinatorial optimization problems. Simply finding the minimum length of wire needed to connect a block of transistors is NP-hard. When factoring in a handful of other simultaneous optimization dimensions such as connections to other blocks of components and area minimization, it is easy to see the difficulty facing circuit designers. Since many of the problems encountered do not have polynomial-time solutions, very large scale integration (VLSI) algorithm designers experiment with various optimization techniques such as integer linear programming and simulated annealing. Optimization methods for VLSI computer-aided design (CAD) incorporating evolutionary search began appearing in research articles in the late 1980’s. As the first body of work devoted exclusively to evolutionary algorithms (EA’s) in VLSI CAD, this monograph attempts to fill a noticeable void in the literature. The book is divided into two parts. Part I “Basic Principles” provides an overview of the book and discusses the underlying principles of EA’s and algorithm performance issues. The treatment of EA’s here is cursory at best and concentrates mainly on the classic genetic algorithm. An overview of some aspects of VLSI CAD is also included; however, only the latter phase of the circuit design process is covered, and analog integrated circuit design is not touched upon. The second part entitled “Practice” comprises roughly threequarters of the book and deals with tools and applications. A software tool called the Genetic Algorithm Managing Environment (GAME) is described in one chapter. It provides a flexible environment in which an algorithm designer can easily interface EA’s to VLSI CAD tools to facilitate experimentation and production runs. One of the author’s themes is that problem-specific knowledge is necessary for EA’s to be competitive with other optimization approaches. Support for this argument is provided by the numerous case studies examined in a lengthy chapter concerning applications of EA’s to logic synthesis, mapping, and testing. In logic synthesis, one wishes to implement a Boolean function in hardware while satisfying certain constraints (e.g., power, delay area). A category of logic realization called Fixed Polarity Reed Muller expressions is described. Such expressions are difficult to minimize and an approach is described that uses a hybrid EA (one that incorporates problem-specific heuristics). For large Boolean functions, it is shown that the EA presented can achieve smaller expressions as compared to standard tools, although the EA required more computer time. The section on mapping includes EA applications in partitioning, floorplanning, and placement and routing problems. Partitioning consists of mapping blocks of circuit components to two-dimensional
Genetic Programming: Turing's Third Way to Achieve Machine Intelligence

One of the central challenges of computer science is to get a computer to solve a problem without explicitly programming it. In particular, it would be desirable to have a problem-independent system whose input is a high-level statement of a problem's requirements and whose output is a working computer program that solves the given problem. The challenge is to make computers do what needs to be done, without having to tell the computer exactly how to do it. Alan Turing recognized that machine intelligence may be realized using a biological approach. In his 1948 essay "Intelligent Machines" , Turing made the connection between search techniques and the challenge of getting a computer to solve a problem without explicitly programming it. Further research into intelligence of machinery will probably be very greatly concerned with “searches” ...
Genetic Programming III: Darwinian Invention & Problem Solving

From the Publisher: 
Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.
roteins as Extracellu Genetic rogramming

As newly sequenced proteins are deposited into the world's ever-growing archive of protein sequences, they are typically immediately tested by various computerized algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location - that is, where the protein resides in a living organism (extracellular, intracellular, etc.). A 1997 paper reported a human-created five-way algorithm for cellular location created using statistical techniques with 76% accuracy. This paper describes a two-way class$cation algorithm that was evolved using genetic programming with 83 % accuracy for determining whether a protein is extracellular. Unlike the statistical calculation, the genetically evolved algorithm employs a large and varied arsenal of computational capabilities, including arithmetic functions, conditional operations, subroutines, iterations, memory, data structures, set-creating operations, macro definitions, recursion, etc. The genetically evolved classification algorithm can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif. The genetically evolved program constitutes an instance of an evolutionary computation technique producing a solution to a problem that is competitive with that produced using human intelligence.
Learning Hierarchical Behaviors

In the last few years, many researchers have beg un to study how to introduce hierarchy into reinforcement learning methods. Generally, th is work has pushed the envelope in one of several important directions, but it typically has voided the question of how to learn systems of hierarchical behavior from experience. We present a preliminary system for learning simple hierarchical systems using a constrained language f or behavior specification. The system learns “do-until” macros by choosing a subgoal from and us ing its past experience with respect to this subgoal to build a constrained macro behavior that achieves it. The system can either be run offline and used for new but similar problems, or c an be used during learning. We present an algorithm that combines macro learning with asynchr onous value iteration methods such as prioritized sweeping. Macros learned by the system for a simple environment are discussed. Additionally, we present the related literature and sketch out the future work for this project.
Evolving Team Darwin United

The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by some level of machine learning. This led, in RoboCup'97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers of those teams. In contrast, our 1998 team was "designed" entirely by the process of genetic programming. Our evolved team placed in the middle of the pack at Robocup98, despite the fact that it was largely machine learned rather than hand coded. This paper presents our motivation, our approach, and the specific construction of our team that created itself from scratch.
Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Cellular Location

As newly sequenced proteins are deposited into the world's ever-growing archives, they are typically immediately tested by various algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location — that is, where the protein resides in a living organism (e.g., extracellular, membrane, nuclear). A human-created five-way algorithm for cellular location using statistical techniques with 76% accuracy was recently reported. This paper describes a two-way algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is an extracellular protein, 84% for nuclear proteins, 89% for membrane proteins, and 83% for anchored membrane proteins. Unlike the statistical calculation, the genetically evolved programs employ a large and varied arsenal of computational capabilities, including arithmetic functions, conditional operations, subroutines, iterations, named memory, indexed memory, setcreating operations, and look-ahead. The genetically evolved classification program can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif.
Evolving computer programs using rapidly reconfigurable field-programmable gate arrays and genetic programming

This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing's H.O.T. Works board) can be exploited to accelerate the time-consuming fitness measurement task of genetic algorithms and genetic programming. This acceleration is accomplished by embodying each individual of the evolving population into hardware in order to perform the fitness measurement task. A 16-step sorting network for seven items was evolved that has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks (and the same number of steps as a 7-sorter that was devised by Floyd and Knuth subsequent to the patent and that is now known to be minimal). Other minimal sorters have been evolved.
On the theory of designing circuits using genetic programming and a minimum of domain knowledge

The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers and computational circuits; however, these previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount of problem-specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.
Evolving Sorting Networks using Genetic Programming an Reconfigurable Xilinx 6216 Field-Programmable Gate Array

This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing Corporation's H.O.T. Works board) can be exploited to accelerate the computationally burdensome fitness measurement task of genetic algorithms and genetic programming. This acceleration is accomplished by embodying each individual of the evolving population into hnrdware in order to perform this time-consuming fitness measurement task. A 16-step sorting network for seven items was evolved that has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks (and the same number of steps as a 7sorter that was devised by Floyd and Knuth subsequent to the patent and that is now known to be minimal).
Evolutionary Design of Analog Electrical Circuits Using Genetic Programming

The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit’s components. There has previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit’s desired behavior. This paper shows how genetic programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller circuit. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.
Classifying proteins as extracellular using programmatic motifs and genetic programming

As newly sequenced proteins are deposited into the world's ever growing archive of protein sequences, they are typically immediately tested by various computerized algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location-that is, where the protein resides in a living organism (extracellular, intracellular, etc.).  reported a human-created five way algorithm for cellular location created using statistical techniques with 76% accuracy. The article describes a two way classification algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is extracellular. Unlike the statistical calculation, the genetically evolved algorithm employs a large and varied arsenal of computational capabilities, including arithmetic functions, conditional operations, subroutines, iterations, memory, data structures, set creating operations, macro definitions, recursion, etc. The genetically evolved classification algorithm can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif. The genetically evolved program constitutes an instance of an evolutionary computation technique producing a solution to a problem that is competitive with that produced using human intelligence.