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58
The GENITOR Algorithm and Selection Pressure: Why RankBased Allocation of Reproductive Trials is Best
 Proceedings of the Third International Conference on Genetic Algorithms
, 1989
"... This paper reports work done over the past three years using rankbased allocation of reproductive trials. New evidence and arguments are presented which suggest that allocating reproductive trials according to rank is superior to fitness proportionate reproduction. Ranking can not only be used to s ..."
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Cited by 329 (12 self)
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This paper reports work done over the past three years using rankbased allocation of reproductive trials. New evidence and arguments are presented which suggest that allocating reproductive trials according to rank is superior to fitness proportionate reproduction. Ranking can not only be used to slow search speed, but also to increase search speed when appropriate. Furthermore, the use of ranking provides a degree of control over selective pressure that is not possible with fitness proportionate reproduction. The use of rankbased allocation of reproductive trials is discussed in the context of 1) Holland's schema theorem, 2) DeJong's standard test suite, and 3) a set of neural net optimization problems that are larger than the problems in the standard test suite. The GENITOR algorithm is also discussed; this algorithm is specifically designed to allocate reproductive trials according to rank.
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 298 (11 self)
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Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores populationbased incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better. Extensions to this algorithm are discussed and analyzed. PBIL and extensions are compared with a standard GA on twelve problems, including standard numerical optimization functions, traditional GA test suite problems, and NPComplete problems.
Evolving Networks: Using the Genetic Algorithm with Connectionist Learning
 In
, 1990
"... It is appealing to consider hybrids of neuralnetwork learning algorithms with evolutionary search procedures, simply because Nature has so successfully done so. In fact, computational models of learning and evolution offer theoretical biology new tools for addressing questions about Nature that hav ..."
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Cited by 179 (2 self)
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It is appealing to consider hybrids of neuralnetwork learning algorithms with evolutionary search procedures, simply because Nature has so successfully done so. In fact, computational models of learning and evolution offer theoretical biology new tools for addressing questions about Nature that have dogged that field since Darwin [Belew, 1990]. The concern of this paper, however, is strictly artificial: Can hybrids of connectionist learning algorithms and genetic algorithms produce more efficient and effective algorithms than either technique applied in isolation? The paper begins with a survey of recent work (by us and others) that combines Holland's Genetic Algorithm (GA) with connectionist techniques and delineates some of the basic design problems these hybrids share. This analysis suggests the dangers of overly literal representations of the network on the genome (e.g., encoding each weight explicitly). A preliminary set of experiments that use the GA to find unusual but successf...
A Review of Evolutionary Artificial Neural Networks
, 1993
"... Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a lot of attention recently. Evolutionary ANNs (EANNs) can be considered as the combination of ANNs and ..."
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Cited by 154 (23 self)
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Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a lot of attention recently. Evolutionary ANNs (EANNs) can be considered as the combination of ANNs and evolutionary search procedures. This paper first distinguishes among three kinds of evolution in EANNs, i.e., the evolution of connection weights, of architectures and of learning rules. Then it reviews each kind of evolution in detail and analyses critical issues related to different evolutions. The review shows that although a lot of work has been done on the evolution of connection weights and of architectures, few attempts have been made to understand the evolution of learning rules. Interactions among different evolutions are seldom mentioned in current research. However, the evolution of learning rules and its interactions with other kinds of evolution play a vital role in EANNs. As t...
Coevolutionary Computation
"... This paper proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE). Two unrelated problems  neural net learning and constraint satisfactio ..."
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Cited by 82 (3 self)
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This paper proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE). Two unrelated problems  neural net learning and constraint satisfaction  are used to illustrate the approach. Both problems use predatorprey interactions to boost the search. In contrast with traditional "single population " genetic algorithms (GAs), two populations constantly interact and coevolve. However, the same algorithm can also be used with different types of coevolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation. The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with co...
A Parallel Genetic Algorithm for the Set Partitioning Problem
, 1994
"... In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed stea ..."
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Cited by 66 (1 self)
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In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steadystate genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steadystate genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty realworld set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, highquality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
Evolutionary Algorithms for Neural Network Design and Training
 IN PROCEEDINGS OF THE FIRST NORDIC WORKSHOP ON GENETIC ALGORITHMS AND ITS APPLICATIONS
, 1995
"... Neural networks and genetic algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. Both models are inspired by nature, but whereas neural networks are concerned with learning of an individual (phenotypic learning), evolutionary algo ..."
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Cited by 43 (1 self)
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Neural networks and genetic algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. Both models are inspired by nature, but whereas neural networks are concerned with learning of an individual (phenotypic learning), evolutionary algorithms deal with a population's adaptation to a changing environment (genotypic learning). This paper focuses on the intersection of neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training. The purpose of the paper is to set forth the general considerations that have to be made when designing an algorithm in this area and to give an overview on how researchers addressed these issues in the past.
Delta Coding: An Iterative Search Strategy for Genetic Algorithms
 Proceedings of the Fourth International Conference on Genetic Algorithms
, 1991
"... A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving an optimization task. Delta coding is a simple search strategy based on the idea that the encoding used ..."
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Cited by 32 (1 self)
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A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving an optimization task. Delta coding is a simple search strategy based on the idea that the encoding used by a genetic algorithm can express a distance away from some previous partial solution. Delta values are added to a partial solution before evaluating the fitness; the delta encoding forms a new hypercube of equal or smaller size that is constructed around the most recent partial solution.
Designing ApplicationSpecific Neural Networks using the Structured Genetic Algorithm.
 In Proceedings of the International Conference on Combinations of Genetic Algorithms and Neural Networks
, 1992
"... We present a different type of genetic algorithm called the Structured Genetic Algorithm (sGA) for the design of applicationspecific neural networks. The novelty of this new genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. This is ..."
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Cited by 32 (2 self)
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We present a different type of genetic algorithm called the Structured Genetic Algorithm (sGA) for the design of applicationspecific neural networks. The novelty of this new genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. This is made possible for sGA primarily due to its redundant genetic material and a gene activation mechanism which in combination provide a multilayered structure to the chromosome. In this paper, we focus on the use of this learning algorithm for automatic generation of a complete application specific neural network. With this approach, no a priori assumptions about topology are needed and the only information required is the input and output characteristics of the task. The empirical studies show that sGA can efficiently determine the network size and topology along with the optimal set of connection weights appropriate for desired tasks, without using backpropagation or any other learning alg...
Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitve Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within ..."
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Cited by 30 (0 self)
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Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform b...