The Lightweight Genetic Search Algorithm: An Efficient Genetic Algorithm for Small Search Range Problems (original) (raw)

An Improved Hybrid Genetic Algorithm with a New Local Search Procedure

Journal of Applied Mathematics, 2013

One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the tradeoff between global and local searching (LS) as it is the case that the cost of an LS can be rather high. This paper proposes a novel, simplified, and efficient HGA with a new individual learning procedure that performs a LS only when the best offspring (solution) in the offspring population is also the best in the current parent population. Additionally, a new LS method is developed based on a three-directional search (TD), which is derivative-free and self-adaptive. The new HGA with two different LS methods (the TD and Neld-Mead simplex) is compared with a traditional HGA. Four benchmark functions are employed to illustrate the improvement of the proposed method with the new learning procedure. The results show that the new HGA greatly reduces the number of function evaluations and converges much faster to the global optimum than a traditional HGA. The TD local search method is a...

A Study of Genetic Algorithm and Crossover Techniques

IJCSMC, 2019

Genetic algorithms are inspired by Darwin's theory of natural evolution. In the natural world, organisms who are poorly suited for the environment die off, while those well suited, prosper. Genetic algorithms search the space of individuals for good candidates. The chance of particular individual being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. Genetic algorithms are ways of solving problems by mimicking processes nature uses; i.e. Selection, Crossover, Mutation and accepting, to evolve a solution to a problem. Many crossover techniques exist for organism which uses different data structures to store themselves. Genetic algorithm which is one of the most well-known heuristic approaches, crossover components and crossover techniques, which are the most important property of the Genetic algorithms performance, has been discussed.

Genetic Algorithm: Reviews, Implementations, and Applications

International Journal of Engineering Pedagogy, 2020

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

A Simple Genetic Algorithm Implementation

Genetic Algorithm offers one of the best solution methods to find an optimal solution for a problem that is unsolvable by a mathematical method. In this paper, Genetic Algorithm is explained with its processes and stages. Moreover, an implementation of Genetic Algorithm to an optimization problem is shown in this paper. Consequences show that, Genetic Algorithm is capable of finding optimal solution really quick.