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117
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 536 (112 self)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
 Evolutionary Computation
, 2000
"... To successfully apply evolutionary algorithms to the solution of increasingly complex problems, we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. One of the major difficulties is finding computational extensions to our current evolutionar ..."
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Cited by 245 (5 self)
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To successfully apply evolutionary algorithms to the solution of increasingly complex problems, we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. One of the major difficulties is finding computational extensions to our current evolutionary paradigms that will enable such subcomponents to “emerge ” rather than being hand designed. In this paper, we describe an architecture for evolving such subcomponents as a collection of cooperating species. Given a simple stringmatching task, we show that evolutionary pressure to increase the overall fitness of the ecosystem can provide the needed stimulus for the emergence of an appropriate number of interdependent subcomponents that cover multiple niches, evolve to an appropriate level of generality, and adapt as the number and roles of their fellow subcomponents change over time. We then explore these issues within the context of a more complicated domain through a case study involving the evolution of artificial neural networks.
Evolutionary Algorithms for Reinforcement Learning
 Journal of Artificial Intelligence Research
, 1999
"... There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are wellknown examples of these approaches. Kaelbling, Littman and Moore recently provided a ..."
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Cited by 105 (1 self)
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There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are wellknown examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problemspecific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications. 1. Introduction Kaelbling, Littman, and Moore (1996) and more recently Sutton and Barto (1998) provide informative surveys of the field of reinforcement learning (RL). They characterize two classes of methods for reinforcement learning: methods that search the space of value fu...
Ideal Evaluation from Coevolution
 Evolutionary Computation
, 2004
"... In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in gameplaying. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult ..."
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Cited by 68 (6 self)
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In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in gameplaying. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult problem in itself, and approximations are likely to introduce human biases into the search process. Coevolution evolves the set of tests used for evaluation, but has so far often led to inaccurate evaluation. We show that for any set of learners, a Complete Evaluation Set can be determined that provides ideal evaluation as specified by Evolutionary MultiObjective Optimization. This provides a principled approach to evaluation in coevolution, and thereby brings automatic ideal evaluation within reach. The Complete Evaluation Set is of manageable size, and progress towards it can be accurately measured. Based on this observation, an algorithm named DELPHI is developed. The algorithm is tested on problems likely to permit progress on only a subset of the underlying objectives. Where all comparison methods result in overspecialization, the proposed method and a variant achieve sustained progress in all underlying objectives. These findings demonstrate that ideal evaluation may be approximated by practical algorithms, and that accurate evaluation for testbased problems is possible even when the underlying objectives of a problem are unknown.
Cooperative Coevolution of MultiAgent Systems
, 2001
"... In certain tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior best be evolved? When the agents are controlled with neural networks, a powerful method is to coevolve them in separate subpopul ..."
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Cited by 53 (4 self)
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In certain tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior best be evolved? When the agents are controlled with neural networks, a powerful method is to coevolve them in separate subpopulations, and test together in the common task. In this paper, such a method, called MultiAgent ESP (Enforced Subpopulations) is presented, and demonstrated in a preycapture task. The approach is shown more efficient and robust than evolving a single central controller for all agents. The role of communication in such domains is also studied, and shown to be unnecessary and even detrimental if effective behavior in the task can be expressed as rolebased cooperation rather than synchronization. 1
CoEvolving a GoPlaying Neural Network
, 2001
"... When evolving a gameplaying neural network, ..."
MetaLearning Evolutionary Artificial Neural Networks
 Journal, Elsevier Science, Netherlands
, 2003
"... In this paper, we present MLEANN (MetaLearning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its param ..."
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Cited by 46 (10 self)
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In this paper, we present MLEANN (MetaLearning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the comparative performance, we used three different wellknown chaotic time series. We also present the state of the art popular neural network learning algorithms and some experimentation results related to convergence speed and generalization performance. We explored the performance of backpropagation algorithm; conjugate gradient algorithm, quasiNewton algorithm and LevenbergMarquardt algorithm for the three chaotic time series. Performances of the different learning algorithms were evaluated when the activation functions and architecture were changed. We further present the theoretical background, algorithm, design strategy and further demonstrate how effective and inevitable is the proposed MLEANN framework to design a neural network, which is smaller, faster and with a better generalization performance.
nozPérez, Multiobjective cooperative coevolution of artificial neural networks (multiobjective cooperative networks), Neural Networks 15
, 2002
"... Abstract—This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a suf ..."
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Cited by 46 (4 self)
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Abstract—This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many realworld problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten realworld classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller. Index Terms—Classification, cooperative coevolution, multiobjective optimization, neural network ensembles. I.