Genetic algorithms GAs are multi dimensional and stochastic search methods involving complex interactions among their parameters For last two decades re searchers have been trying to understand the mechanics of GA parameter interac tions by using various techniques careful functional decomposition of parameter interactions empirical studies and Markov chain analysis Although the complex ities i...

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it ...

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it ...

We present a covariant form for the dynamics of a canonical GA of arbitrary cardinality, showing how each genetic operator can be uniquely represented by a mathematical object - a tensor - that transforms simply under a general linear coordinate transformation. For mutation and recombination these tensors can be written as tensor products of the analogous tensors for one-bit strings thus giving...

<p>The genetic algorithm (GA) is an adaptive metaheuristic search method based on the process of evolution and natural selection theory. It efficient used for solving combinatorial optimization problems, e.g., travel salesman problem (TSP), linear ordering (LOP), job-shop scheduling (JSP). The simple GA applied takes a long time to reach optimal solution, configuration parameters vital su...

Large high-clearance sprayers are widely used in the field of plant protection due to their high work efficiency. Influenced by characteristics a large ground clearance, fast driving speed and constantly changing sprung mass, how solve contradiction between vibration reduction performance sprayer friendliness farmland roads has become current research hotspot. In order improve sprayers, design,...

This paper introduces an alternative approach to relieving the task of choosing optimal mutation and crossover rates by using a parallel and distributed GA with distributed environments. It is shown that the best mutation and crossover rates depend on the population sizes and the problems, and those are different between a single and multiple populations. The proposed distributed environment GA...

In this case study we consider the set covering problem, the classical operations research problem of covering the rows of a zero-one matrix by a subset of the columns at minimum cost. We outline a genetic-algorithm-based heuristic for the problem. The key features of this genetic algorithm are a binary representation, drawn naturally from a zero-one formulation of the problem, a new crossover ...

The optimal table row and column ordering can reveal useful patterns to improve reading and interpretation. Recently, genetic algorithms using standard crossover and mutation operators have been proposed to tackle this problem. In this paper, we carry out an experimental study that adds to this genetic algorithm crossover and mutation operators specially designed to deal with permutations and i...