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A Genetic Algorithm Tutorial
 Statistics and Computing
, 1994
"... This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorit ..."
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Cited by 231 (5 self)
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This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
Niching Methods for Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This ..."
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Cited by 191 (1 self)
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Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...
Evaluating Evolutionary Algorithms
 Artificial Intelligence
, 1996
"... Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing se ..."
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Cited by 86 (14 self)
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Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hillclimbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of...
A Promising Genetic Algorithm Approach to JobShop Scheduling, Rescheduling, and OpenShop Scheduling Problems
 Proceedings of the Fifth International Conference on Genetic Algorithms
, 1993
"... The general jobshop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark jobshop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing con ..."
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Cited by 79 (2 self)
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The general jobshop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark jobshop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional searchbased methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the openshop scheduling problem and the jobshop rescheduling problem. 1 INTRODUCTION The jobshop scheduling problem (JSSP) is a very important practical problem. Efficient methods of solving it can have major effects on profitability and product quality, but with the JSSP being amon...
An Overview of Genetic Algorithms: Part 1, Fundamentals
, 1993
"... this article may be reproduced for commercial purposes. 1 Introduction ..."
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Cited by 79 (1 self)
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this article may be reproduced for commercial purposes. 1 Introduction
Genetic Set Recombination and its Application to Neural Network Topology Optimisation
 NEURAL COMPUTING AND APPLICATIONS
, 1993
"... Forma analysis is applied to the task of optimising the connectivity of a feedforward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed and variablesi ..."
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Cited by 66 (3 self)
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Forma analysis is applied to the task of optimising the connectivity of a feedforward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed and variablesize sets and multisets. These techniques require a further generalisation of the notion of gene, which is presented. The result is a nonredundant representation of the neural network topology optimisation problem together with recombination operators which have carefully designed and wellunderstood properties. The techniques developed have relevance to the application of genetic algorithms to constrained optimisation problems.
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.
Fitness Variance of Formae and Performance Prediction
, 1994
"... Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated ..."
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Cited by 61 (7 self)
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Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representationindependent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling salesrep problem (TSP) in the context of both formal representationindependentgenetic algorithms and corresponding memetic algorithms. 1 Motivation The subject of this paper is representation i...
Building Better Test Functions
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... We introduce basic guidelines for developing test suites for evolutionary algorithms and examine common test functions in terms of these guidelines. Two methods of designing test functions are introduced which address specific issues relevant to comparative studies of evolutionary algorithms. ..."
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Cited by 54 (5 self)
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We introduce basic guidelines for developing test suites for evolutionary algorithms and examine common test functions in terms of these guidelines. Two methods of designing test functions are introduced which address specific issues relevant to comparative studies of evolutionary algorithms. The first method produces representation invariant functions.