Crossover is one of the three basic operators in any genetic algorithm (GA). Several
crossover techniques have been proposed and their relative merits are currently under …

The genetic algorithm (GA) is a new optimization paradigm that models a natural evolution
mechanism. The framework of the GA naturally corresponds to a discrete optimization …

The performance of genetic algorithms (GA) is affected by various factors such as
coefficients and constants, genetic operators, parameters and some strategies. Member …

A new approach to optimization design concerning the configurations of structures using
genetic algorithm (GA) with a tournament selection strategy has been proposed. The …

This paper discusses the adaptive approach in genetic algorithms (GAs). It is tried to show
how the adaptive approach affects the performance of GAs, suggesting some improvements …

Abstract The efficiency of Simple Genetic Algorithm (SGA) can be improved by some
strategies. They are elitest strategy, multi‐point crossover, identification of passive design …

Optimizing most structural systems used in practice requires considering design variables as
discrete quantities. The paper presents a simple genetic algorithm for optimizing structural …

Although the genetic algorithm (GA) for structural optimization is very robust, it is very
computationally intensive and hence slower than optimality criteria and mathematical …

This paper presents an optimization algorithm based on Simulated Annealing. The algorithm–
denoted as CMLPSA (Corrected Multi-Level &Multi-Point Simulated Annealing)–implements …

A regional genetic algorithm (R‐GA) is used for the discrete optimal design of truss
structures. The chromosomes are selected from a sub‐region centred on the continuous …