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15
Perspectives: Complex Adaptations and the Evolution of Evolvability
, 1996
"... The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian p ..."
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Cited by 223 (8 self)
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The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess "evolvability", i.e. the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotypephenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variation, which are the actually realized differences between individuals. The genotypephenotype map is the ...
The Science of Breeding and its Application to the Breeder Genetic Algorithm BGA
 EVOLUTIONARY COMPUTATION
, 1994
"... The Breeder Genetic Algorithm BGA models artificial selection as performed by human breeders. The science of breeding is based on advanced statistical methods. In this paper a connection between genetic algorithm theory and the science of breeding is made. We show how the response to selection eq ..."
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Cited by 115 (23 self)
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The Breeder Genetic Algorithm BGA models artificial selection as performed by human breeders. The science of breeding is based on advanced statistical methods. In this paper a connection between genetic algorithm theory and the science of breeding is made. We show how the response to selection equation and the concept of heritability can be applied to predict the behavior of the BGA. Selection, recombination and mutation are analyzed within this framework. It is shown that recombination and mutation are complementary search operators. The theoretical results are obtained under the assumption of additive gene effects. For general fitness landscapes regression techniques for estimating the heritability are used to analyze and control the BGA. The method of decomposing the genetic variance into an additive and a nonadditive part connects the case of additive fitness functions with the general case.
Empirical Modeling of Genetic Algorithms
 EVOLUTIONARY COMPUTATION
, 2001
"... This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a gra ..."
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Cited by 8 (1 self)
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This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graecolatin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models thus derived can be used first to determine optimal algorithm parameters, and second to shed light on interactions between the parameters and their relative importance. The initial models do not extrapolate well. However, an advantage of this approach is that the modelling process is under the control of the experimenter, and is hence very flexible. Refined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.
A Comparative Analysis of Evolutionary Algorithms for Function Optimisation
 IN PROCS. OF THE SECOND WORKSHOP ON EVOLUTIONARY COMPUTING (WEC2
, 1996
"... In this paper the Breeder Genetic Algorithms are compared against both serial and parallel Genetic Algorithms by using a wide range of optimisation functions taken from the literature. The aim is to investigate how the change of the fitness function influences their problem solving capabilities. T ..."
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Cited by 8 (0 self)
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In this paper the Breeder Genetic Algorithms are compared against both serial and parallel Genetic Algorithms by using a wide range of optimisation functions taken from the literature. The aim is to investigate how the change of the fitness function influences their problem solving capabilities. The experimental findings show that the Breeder Genetic Algorithms outperform the Genetic Algorithms considered in terms of number of evaluations and robustness. I. Introduction A wide variety of heuristic techniques has been proposed to solve different kinds of optimisation problems. Among these, the Genetic Algorithms (GAs) [1; 2] and their parallel versions [3] should be mentioned. They have proved to be able to efficiently solve difficult optimisation problems. Recently a novel technique, the Breeder Genetic Algorithms (BGAs) [4; 5], particularly suitable to deal with continuous optimisation parameters, has been introduced. BGAs are based on the evolution model typical of GAs, however th...
Optimizing Neural Networks for Time Series Prediction
 Third World Conference on Soft Computing (WSC3
, 1998
"... In this paper we investigate the effective design of an appropriate neural network model for time series prediction based on an evolutionary approach. In particular, the Breeder Genetic Algorithms are considered to face contemporaneously the optimization of (i) the design of a neural network archite ..."
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Cited by 6 (0 self)
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In this paper we investigate the effective design of an appropriate neural network model for time series prediction based on an evolutionary approach. In particular, the Breeder Genetic Algorithms are considered to face contemporaneously the optimization of (i) the design of a neural network architecture and (ii) the choice of the best learning method. The effectiveness of the approach proposed is evaluated on a standard benchmark for prediction models, the MackeyGlass series. 1. Introduction The main motivation for time series research is to provide a prediction when a mathematical model of a phenomenon is either unknown or incomplete. A time series consists of measurements or observations of the previous outcomes of a phenomenon that are made sequentially over time. If these consecutive observations are dependent on each other then it is possible to attempt a prediction. Clearly it is supposed that the process is somehow predictable. The time series prediction problems are usually...
Revisiting Bremermann's Genetic Algorithm: I. Simultaneous Mutation of All Parameters
 Applications and Science of Computational Intelligence IV
, 2000
"... Hans Bremermann was one of the pioneers of evolutionary computation. Many of his early suggestions for designing evolutionary algorithms anticipated future inventions, including scaling mutations to be inversely proportional to the number of parameters in the problem as well as many forms of recombi ..."
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Cited by 5 (1 self)
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Hans Bremermann was one of the pioneers of evolutionary computation. Many of his early suggestions for designing evolutionary algorithms anticipated future inventions, including scaling mutations to be inversely proportional to the number of parameters in the problem as well as many forms of recombination. This paper explores the gain in performance that occurs when Bremermann's original evolutionary algorithm is extended to include the simultaneous mutation of every component in a candidate solution. Bremermann's original perspective was entirely "genetic," where each component corresponded to a gene and therefore multiple simultaneous changes were viewed as occuring with geometrically decreasing probability. Experiments indicate that a change in perspective to a "phenotypic" view, where all components change at once, can lead to more rapid optimization on linear systems of equations.
Evolutionary MultiObjective Decision Support Systems for Conceptual Design
, 2000
"... In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi–objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and variou ..."
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Cited by 3 (0 self)
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In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi–objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA–like approaches etc. Large number of runs are performed for finding the optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real–world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as “more important”, “much less important ” etc.) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: ¯in weighted sum based optimisation methods; ¯in weighted Pareto method;
Intra and ExtraGeneration Schemes for Combining Crossover Operators
"... Several studies on the variations of the crossover operator have shown that each of them presents specic properties that are interesting under particular circumstances. The advantages of each operator over others are often contradictory and the best operator depends on the problem being solved. ..."
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Cited by 3 (2 self)
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Several studies on the variations of the crossover operator have shown that each of them presents specic properties that are interesting under particular circumstances. The advantages of each operator over others are often contradictory and the best operator depends on the problem being solved. This paper is based on the assumption that a combination of several crossover operators can take advantage of their respective qualities and power. Thus, we propose several combination models based on four crossover operators: the 1point, 2point, uniform and dissociated. We test the performance of these models through an experimental approach using the problem of the Hamiltonian circuit.
Investigating a Parallel Breeder Genetic Algorithm on the Inverse Aerodynamic Design
 In Voigt et al. [574
, 1996
"... . Breeder Genetic Algorithms represent a class of random optimisation techniques gleaned from the science of population genetics, which have proved their ability to solve hard optimisation problems with continuous parameters. In this paper we test a parallel version of this technique against a seque ..."
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Cited by 2 (1 self)
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. Breeder Genetic Algorithms represent a class of random optimisation techniques gleaned from the science of population genetics, which have proved their ability to solve hard optimisation problems with continuous parameters. In this paper we test a parallel version of this technique against a sequential Breeder Genetic Algorithm on a typical inverse design problem in Aerodynamics, the problem of an aerofoil geometry recover starting from a target pressure distribution. Our results show that Parallel Breeder Genetic Algorithms are well suited for applications in Aerodynamics. Keywords: Breeder Genetic Algorithms, Aerodynamic Design, Parallel Genetic Algorithms. 1 Introduction There are two problems in Aerodynamics: the analysis and the design [1, 2]. The former (direct problem) consists in describing a flow field given the geometry of the object, whereas the latter (inverse problem) requires to find the geometry of the object causing the given flow field. As an example of the second c...