Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms
Proceedings of the Third European Conference on Advances in Artificial Life
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Metabits: Generic Endogenous Crossover Control
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptive Crossover Using Automata
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Controlling Crossover through Inductive Learning
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Explicit Filtering of Building Blocks for Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An Adaptive Poly-Parental Recombination Strategy
Selected Papers from AISB Workshop on Evolutionary Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The equation for response to selection and its use for prediction
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Convergence Time for the Linkage Learning Genetic Algorithm
Evolutionary Computation
ERA: an algorithm for reducing the epistasis of SAT problems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
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A number of algorithms have been proposed aimed at tackling the problem of learning “Gene Linkage” within the context of genetic optimisation, that is to say, the problem of learning which groups of co-adapted genes should be inherited together during the recombination process. These may be seen within a wider context as a search for appropriate relations which delineate the search space and “guide” heuristic optimisation, or, alternatively, as a part of a comprehensive body of work into Adaptive Evolutionary Algorithms.In this paper, we consider the learning of Gene Linkage as an emergent property of adaptive recombination operators. This is in contrast to the behaviour observed with fixed recombination strategies in which there is no correspondence between the sets of genes which are inherited together between generations, other than that caused by distributional bias. A discrete mathematical model of Gene Linkage is introduced, and the common families of recombination operators, along with some well known linkage-learning algorithms, are modelled within this framework. This model naturally leads to the specification of a recombination operator that explicitly operates on sets of linked genes.Variants of that algorithm, are then used to examine one of the important concepts from the study of adaptivity in Evolutionary Algorithms, namely that of the level (population, individual, or component) at which learning takes place. This is an aspect of adaptation which has received considerable attention when applied to mutation operators, but which has been paid little attention in the context of adaptive recombination operators and linkage learning. It is shown that even with the problem restricted to learning adjacent linkage, the population based variants are not capable of correctly identifying building blocks. This is in contrast to component level adaptation which outperforms conventional operators whose bias is ideal for the problems considered.