Evolutionary computation and Wright's equation

Authors:
H. Mühlenbein;Th. Mahnig
Affiliations:
Real World Computing Partnership---RWCP, Theoretical Foundation, GMD--Forschungszentrum Informationstechnik Laboratory D-53754 Sankt Augustin, Germany;Real World Computing Partnership---RWCP, Theoretical Foundation, GMD--Forschungszentrum Informationstechnik Laboratory D-53754 Sankt Augustin, Germany
Venue:
Theoretical Computer Science - Natural computing
Year:
2002

Citing 5
Cited 10

Learning in graphical models

Learning in graphical models
Evolutionary algorithms: from recombination to search distributions

Theoretical aspects of evolutionary computing
Schemata, Distributions and Graphical Models in Evolutionary Optimization

Journal of Heuristics
The equation for response to selection and its use for prediction

Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions

Evolutionary Computation

Effective mutation rate for probabilistic evolutionary design of analogue electrical circuits

Applied Soft Computing
Multi-objective univariate marginal distribution optimisation of mixed analogue-digital signal circuits

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Short Communication: Feedback linearization control of a two-link robot using a multi-crossover genetic algorithm

Expert Systems with Applications: An International Journal
On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies

EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Why one must use reweighting in estimation of distribution algorithms

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Application of the Univariate Marginal Distribution Algorithm to Mixed Analogue - Digital Circuit Design and Optimisation

Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Node level crossover applied to neural network evolution

IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Comparison-based complexity of multiobjective optimization

Proceedings of the 13th annual conference on Genetic and evolutionary computation
A study on the global convergence time complexity of estimation of distribution algorithms

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Tracking property of UMDA in dynamic environment by landscape framework

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, Wright's equation formulated in 1931 is proven and applied to evolutionary computation. Wright's equation shows that evolution is doing gradient ascent in a landscape defined by the average fitness of the population. The average fitness W is defined in terms of marginal gene frequencies pi. Wright's equation is only approximately valid in population genetics, but it exactly describes the behavior of our univariate marginal distribution algorithm (UMDA). We apply Wright's equation to a specific fitness function defined by Wright. Furthermore we introduce mutation into Wright's equation and UMDA. We show that mutation moves the stable attractors from the boundary into the interior. We compare Wright's equation with the diversified replicator equation. We show that a fast version of Wright's equation gives very good results for optimizing a class of binary fitness functions.