Improvement Of Generation Change On
SSE Algorithm
Author(s)
T. Maruyama & E. Kita
Abstract
The Stochastic Schemata Exploiter (SSE) is one of the evolutionary optimization
algorithms for solving the combinatorial optimization problems. For improving
the search performance of SSE, we will present the cross generational elitist
selection SSE (cSSE) algorithms of which the generation alternation algorithm
is improved by using cross generational elitist selection. The SSE and the
cSSE are compared with the Minimal Generation Gap (MGG) and the Bayesian
Optimization Algorithm (BOA) on deception and knapsack problems in order to
discuss their features.
1 Introduction
The actual industrial problems are often formulated as the multi-objective
optimization problems defined with many design variables. Since it is very time-consuming
to find the solutions exactly, we have to obtain the quasi-optimum
solutions in an admissible time. For this purpose, the evolutionary computations
(ECs) are very useful tool [1–4].
Stochastic Schemata Exploiter (SSE) was presented by Aizawa [5] in 1994.
The SSE is one of the EC and effective for the 0/1 combinational optimization
problems. In the Genetic Algorithm(GAs), the individuals are generated randomly
and the new ones are generated by the genetic operations such as the mutation,
the crossover, the selection and so on. In the SSE, the other operations than the
mutation are not necessary. Therefore, the total number of the control parameter of
the SSE is much smaller than that of original GAs. Sub-populations are generated
from the whole population according to the fitness ranking of the individuals.
Schemata are extracted from the sub-population. New individuals are generated
Keywords
Related Book
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