Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm

Voigt, Hans-Michael; Mühlenbein, Heinz

doi:10.1109/icec.1995.489139

Cited by 25 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A principal component, analysis-based construction of representation for performing evolutionary optimization has been developed elsewhere (Voigt and Mühlenbein, 1995). Kazadi (1997) proposed a similar approach to explicitly detect generalized schemata that he calls conjugate schemata.…”

Section: Application Of Dr2 In Linkage Learningmentioning

confidence: 99%

Gene Expression and Fast Construction of Distributed Evolutionary Representation

Kargupta

Park

2001

Evolutionary Computation

View full text Add to dashboard Cite

The gene expression process in nature produces different proteins in different cells from different portions of the DNA. Since proteins control almost every important activity in a living organism, at an abstract level, gene expression can be viewed as a process that evaluates the merit or “fitness” of the DNA. This distributed evaluation of the DNA would not be possible without a decomposed representation of the fitness function defined over the DNAs. This paper argues that, unless the living body was provided with such a representation, we have every reason to believe that it must have an efficient mechanism to construct this distributed representation. This paper demonstrates polynomial-time computability of such a representation by proposing a class of efficient algorithms. The main contribution of this paper is two-fold. On the algorithmic side, it offers a way to scale up evolutionary search by detecting the underlying structure of the search space. On the biological side, it proves that the distributed representation of the evolutionary fitness function in gene expression can be computed in polynomial-time. It advances our understanding about the representation construction in gene expression from the perspective of computing. It also presents experimental results support-ing the theoretical performance of the proposed algorithms.

show abstract

Section: Application Of Dr2 In Linkage Learningmentioning

confidence: 99%

Gene Expression and Fast Construction of Distributed Evolutionary Representation

Kargupta

Park

2001

Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…T h e technique of first doing a PCA and then performing gene pool recornbination in the transformed space has been successfully used for the optimization of difficult fitness functions by Voigt and Miihlenbein (1995). It is interesting to note that in evolution strategies, the need for second-order models was recognized very early (Back & Schwefel, 1993).…”

Section: From Recombination To Estimation Of Distributionsmentioning

confidence: 99%

The Equation for Response to Selection and Its Use for Prediction

Mühlenbein¹

1997

Evolutionary Computation

434

213

View full text Add to dashboard Cite

The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA, and the average fitness. We compare Mendelian two-parent recombination with gene-pool recombination, which belongs to a special class of genetic algorithms that we call univariate marginal distribution (UMD) algorithms. UMD algorithms keep the genotypes in linkage equilibrium. For UMD algorithms, an exact RS equation is proven that can be used for long-term prediction. Empirical and theoretical evidence is provided that indicates that Mendelian two-parent recombination is also mainly exploiting the additive genetic variance. We compute an exact RS equation for binary tournament selection. It shows that the two classical methods for estimating realized heritability—the regression heritability and the heritability in the narrow sense—may give poor estimates. Furthermore, realized heritability for binary tournament selection can be very different from that of proportionate selection. The paper ends with a short survey about methods that extend standard genetic algorithms and UMD algorithms by detecting interacting variables in nonlinear fitness functions and using this information to sample new points.

show abstract

“…There is of course no necessity to be restricted to two-parent recombination, as EAs allow us to emulate the ideas of natural evolution very flexibly. A few attempts to study the effect of using more than two parents for recombination in EAs are reported in the literature [1,2,6,7,8,9,19,20,23].…”

Section: Introductionmentioning

confidence: 99%

Multi-parent recombination in genetic algorithms with search space boundary extension by mirroring

Tsutsui

1998

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In previous work, we have investigated real coded genetic algorithms with several types of multi-parent recombination operators and found evidence that multi-parent recombination with center of mass crossover (CMX) seems a good choice for real coded GAs. But CMX does not work well on functions which have their optimum on the corner of the search space. In this paper, we propose a method named boundary extension by mirroring (BEM) to cope with this problem. Applying BEM to CMX, the performance of CMX on the test functions which have their optimum on the corner of the search space was much improved. Further, by applying BEM, we observed clear improvement in performance of two-parent recombination on the functions which have their optimum on the corner of the search space. Thus, we suggest that BEM is a good general technique to improve the efficiency of crossover operators in real-coded GAs for a wide range of functions.

show abstract

Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm

Cited by 25 publications

References 6 publications

Gene Expression and Fast Construction of Distributed Evolutionary Representation

Gene Expression and Fast Construction of Distributed Evolutionary Representation

The Equation for Response to Selection and Its Use for Prediction

Multi-parent recombination in genetic algorithms with search space boundary extension by mirroring

Contact Info

Product

Resources

About