Estimation And Extension Of The
Stochastic Schemata Exploiter
Author(s)
T. Maruyama & E. Kita
Abstract
In this paper, the Stochastic Schemata Exploiter (SSE) is evaluated by seven test
problems. The results indicate that the convergence property of the SSE is better
than that of the simple genetic algorithm. Next, the Extended Stochastic Schemata
Exploiter (ESSE) is introduced and compared with the SSE by the knapsack problem.
The results indicate that some of the ESSE algorithms have better convergence
properties than the SSE.
1 Introduction
The Stochastic Schemata Exploiter (SSE) has been presented by Aizawa [1]. The
SSE algorithmis considered to be one of the evolutionary algorithms, and designed
for solving the optimization problem of which the design variables are defined as
binary numbers. In traditional simple genetic algorithms (SGAs), the individuals
of which the fitness values are bigger survive at the next generation. In the SSE, the
schemata extracted from the individuals in the population are ranked and the higher
ranked schemata survive at the next generation. In this paper, the performance of
the SSE algorithm is evaluated by seven test problems; the deception problem, the
knapsack problem and the solution search of Rastrigin, Rosenbrock, Griewank,
Ridge, and Schwefel functions [2]. After that, we will present the extended SSE
(ESSE) algorithm which is composed of the original SSE and new ESSE operations.
The operations are added to the SSE in order to improve the diversity of the
individuals. Finally, the ESSE and the SSE are compared in the Knapsack problem.
Keywords
Related Book
Other papers in this volume
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