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62
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...
Simulated Binary Crossover for Continuous Search Space
, 1994
"... The success of binarycoded genetic algorithms (GAs) in problems having discrete search space largely depends on the coding used to represent the problem variables and on the crossover operator that propagates buildingblocks from parent strings to children strings. In solving optimization problems ..."
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Cited by 126 (26 self)
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The success of binarycoded genetic algorithms (GAs) in problems having discrete search space largely depends on the coding used to represent the problem variables and on the crossover operator that propagates buildingblocks from parent strings to children strings. In solving optimization problems having continuous search space, binarycoded GAs discretize the search space by using a coding of the problem variables in binary strings. However, the coding of realvalued variables in finitelength strings causes a number of difficultiesinability to achieve arbitrary precision in the obtained solution, fixed mapping of problem variables, inherent Hamming cliff problem associated with the binary coding, and processing of Holland's schemata in continuous search space. Although, a number of realcoded GAs are developed to solve optimization problems having a continuous search space, the search powers of these crossover operators are not adequate. In this paper, the search power of a cross...
Tackling RealCoded Genetic Algorithms: Operators and Tools for Behavioural Analysis
 Artificial Intelligence Review
, 1998
"... . Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of ..."
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Cited by 123 (24 self)
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. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of realcoded genetic algorithms. Different models of genetic operators and some me...
Equivalence Class Analysis Of Genetic Algorithms
 COMPLEX SYSTEMS
, 1991
"... The conventional understanding of genetic algorithms depends upon analysis by schemata and the notion of intrinsic parallelism. For this reason, only kary string representations have had any formal basis and nonstandard representations and operators have been regarded largely as heuristics, rather ..."
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Cited by 101 (8 self)
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The conventional understanding of genetic algorithms depends upon analysis by schemata and the notion of intrinsic parallelism. For this reason, only kary string representations have had any formal basis and nonstandard representations and operators have been regarded largely as heuristics, rather than principled algorithms. This paper extends the analysis to general representations through identification of schemata as equivalence classes induced by implicit equivalence relations over the space of chromosomes.
Deception Considered Harmful
 Foundations of Genetic Algorithms 2
, 1992
"... A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of Deception, defined in terms of the static average fitness of competing schemas. This article examines t ..."
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Cited by 73 (0 self)
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A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of Deception, defined in terms of the static average fitness of competing schemas. This article examines the Static Building Block Hypothesis (SBBH), the underlying assumption used to define Deception. Exploiting contradictions between the SBBH and the Schema Theorem, we show that Deception is neither necessary nor sufficient for problems to be difficult for GAs. This article argues that the characterization of hard problems must take into account the basic features of genetic algorithms, especially their dynamic, biased sampling strategy. Keywords: Deception, building block hypothesis 1 INTRODUCTION Since Holland's early work on the analysis of genetic algorithms (GAs), the usual approach has been to focus on the allocation of search effort to subspaces described by schemas representing hyper...
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.
SelfAdaptive Genetic Algorithms with Simulated Binary Crossover
 COMPLEX SYSTEMS
, 1999
"... Selfadaptation is an essential feature of natural evolution. However, in the context of function optimization, selfadaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the selfa ..."
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Cited by 66 (10 self)
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Selfadaptation is an essential feature of natural evolution. However, in the context of function optimization, selfadaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the selfadaptive feature of realparameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of selfadaptive ESs and realparameter GAs with SBX operator is also discussed. Thereafter, the selfadaptive behavior of realparameter GAs is demonstrated on a number of test problems commonlyused in the ES literature. The remarkable similarity in the working principle of realparameter GAs and selfadaptive ESs shown in this study suggests the need of emphasizing further studies on selfadaptive GAs.
Optimizing globallocal search hybrids
 In GECCO
, 1999
"... This paper develops a framework for optimizing globallocal hybrids of search or optimization procedures. The paper starts by idealizing the search problem as a search by a global algorithm G for either (1) acceptable targetssolutions that meet a speci ed criterionor for (2) basins of attraction t ..."
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Cited by 39 (11 self)
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This paper develops a framework for optimizing globallocal hybrids of search or optimization procedures. The paper starts by idealizing the search problem as a search by a global algorithm G for either (1) acceptable targetssolutions that meet a speci ed criterionor for (2) basins of attraction that then lead to acceptable targets under a speci ed local search algorithm L. The paper continues by abstracting two sets of parametersprobabilities of successfully hitting targets and basins and timetocriterion coe cientsand writing equations to account for the total time of search and for the reliability in reaching an acceptable solution. A twobasin optimality criterion is derived and applied to important representative problems. Continuations and extensions of the work are suggested, but the theory appears to be useful immediately in better understanding the economy of e ective hybridization. 1
Using RealValued Genetic Algorithms to Evolve Rule Sets for Classification
 In IEEECEC
, 1994
"... In this paper, we use a genetic algorithm to evolve a set of classification rules with realvalued attributes. We show how realvalued attribute ranges can be encoded with realvalued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification ..."
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Cited by 31 (1 self)
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In this paper, we use a genetic algorithm to evolve a set of classification rules with realvalued attributes. We show how realvalued attribute ranges can be encoded with realvalued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to geneticbased machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system. I. Introduction Genetic algorithms (GAs) have proved to be robust, domain independent mechanisms for numeric and symbolic optimization[7]. Our previous work has demonstrated effective geneticbased rule learning in discrete domains [13]. In the real world, however, most classification prob...
NonLinear Genetic Representations
, 1992
"... The limitations of linear chromosomes and conventional recombination operators are reviewed. It is argued that there are at least three classes of problems for which such representations and operators are likely to be ineffective. Methods for constructing operators which manipulate more complex stru ..."
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Cited by 29 (2 self)
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The limitations of linear chromosomes and conventional recombination operators are reviewed. It is argued that there are at least three classes of problems for which such representations and operators are likely to be ineffective. Methods for constructing operators which manipulate more complex structures with evolutionary search methods are presented, and it is argued that whenever possible, genetic operators and analogues of schemata should be defined directly in space of phenotypes, rather than in the genotype (representation) space.