Results 1  10
of
98
Evolutionary computation: Comments on the history and current state
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1997
"... Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and ..."
Abstract

Cited by 217 (0 self)
 Add to MetaCart
Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.
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 ..."
Abstract

Cited by 131 (25 self)
 Add to MetaCart
. 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...
The Schema Theorem and Price's Theorem
 FOUNDATIONS OF GENETIC ALGORITHMS
, 1995
"... Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implicati ..."
Abstract

Cited by 95 (3 self)
 Add to MetaCart
Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata reemerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is exa...
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 ..."
Abstract

Cited by 83 (3 self)
 Add to MetaCart
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...
Crossover or Mutation?
 Foundations of Genetic Algorithms 2
, 1992
"... Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mu ..."
Abstract

Cited by 72 (3 self)
 Add to MetaCart
Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.
The Algebra of Genetic Algorithms
, 1994
"... A rigorous formulation of the generalisation of schema analysis known as forma analysis is presented. This is shown to provide a direct mechanism for harnessing knowledge about a search space, codified through the imposition of equivalence relations over that space, to generate a genetic represen ..."
Abstract

Cited by 64 (10 self)
 Add to MetaCart
A rigorous formulation of the generalisation of schema analysis known as forma analysis is presented. This is shown to provide a direct mechanism for harnessing knowledge about a search space, codified through the imposition of equivalence relations over that space, to generate a genetic representation and operators. It is shown that a single characterisation of a space leads to a unique genetic representation, and the kinds of representations that are possible are classified and discussed. A relatively new operator, call random assorting recombination (RARw ), is defined rigorously and is shown to be, in an important sense, a universal recombination operator.
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
 Foundation of Intelligent Systems 9th International Symposium, ISMIS '96
, 1996
"... . The role of the mutation rate in canonical genetic algorithms is investigated by comparing a constant setting, a deterministically varying, timedependent mutation rate schedule, and a selfadaptation mechanism for individual mutation rates following the principle of selfadaptation as used in evo ..."
Abstract

Cited by 54 (0 self)
 Add to MetaCart
. The role of the mutation rate in canonical genetic algorithms is investigated by comparing a constant setting, a deterministically varying, timedependent mutation rate schedule, and a selfadaptation mechanism for individual mutation rates following the principle of selfadaptation as used in evolution strategies. The power of the selfadaptation mechanism is illustrated by a timevarying optimization problem, where mutation rates have to adapt continuously in order to follow the optimum. The strengths of the proposed deterministic schedule and the selfadaptation method are demonstrated by a comparison of their performance on difficult combinatorial optimization problems (multiple knapsack, maximum cut and maximum independent set in graphs). Both methods are shown to perform significantly better than the canonical genetic algorithm, and the deterministic schedule yields the best results of all control mechanisms compared. 1 Introduction Genetic Algorithms [11, 14] are the best kno...
Designer Genetic Algorithms: Genetic Algorithms in Structure Design
 Proceedings of the Fourth International Conference on Genetic Algorithms
, 1991
"... This paper considers the problem of using genetic algorithms to design structures. We relax one constraint on classical genetic algorithms and describe a genetic algorithm that uses differential information about search direction to design structures. This differential information is captured by a m ..."
Abstract

Cited by 38 (7 self)
 Add to MetaCart
This paper considers the problem of using genetic algorithms to design structures. We relax one constraint on classical genetic algorithms and describe a genetic algorithm that uses differential information about search direction to design structures. This differential information is captured by a masked crossover operator which also removes the bias toward short schemas. We analyze performance and present some preliminary results. Further, consideration of this problem suggests a partial solution to the identification of the deception problem. 1 INTRODUCTION The problem of designing structures is pervasive in science and engineering. The problem is: Given a function and some materials to work with, design a structure that performs this function subject to certain constraints. As an example of the design problem, consider the combinational circuit design problem: Given a set of logic gates, design a circuit that performs a desired function. Two instantiations of this problem are the...
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 ..."
Abstract

Cited by 31 (2 self)
 Add to MetaCart
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.