The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Evolutionary Computation
Simple genetic algorithms with linear fitness
Evolutionary Computation
Modeling simple genetic algorithms
Evolutionary Computation
The simple genetic algorithm and the walsh transform: Part i, theory
Evolutionary Computation
Form Invariance and Implicit Parallelism
Evolutionary Computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Crossover Invariant Subsets of the Search Space for Evolutionary Algorithms
Evolutionary Computation
Evolutionary Computation
Detecting the epistatic structure of generalized embedded landscape
Genetic Programming and Evolvable Machines
Theory of the simple genetic algorithm with α-selection
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Intrinsic System Model of the Genetic Algorithm with α-Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An Analysis of Recombination in Some Simple Landscapes
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Natural coding: a more efficient representation for evolutionary learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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This paper continues the development, begun in Part I, of the relationship between the simple genetic algorithm and the Walsh transform. The mixing scheme (comprised of crossover and mutation) is essentially “triangularized” when expressed in terms of the Walsh basis. This leads to a formulation of the inverse of the expected next generation operator. The fixed points of the mixing scheme are also determined, and a formula is obtained giving the fixed point corresponding to any starting population. Geiringer's theorem follows from these results in the special case corresponding to zero mutation.