Three Proposed Hybrid Genetic Algorithms

A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching s...

Modeling Hybrid Genetic Algorithms

Hybrid Genetic Algorithms and Clustering

This paper introduces a hybrid genetic algorithm that uses fuzzy c-means clustering technique as a mechanism to reduce fitness evaluations and to preserve solution quality. Population clustering provides a means to evaluate only the representative individual of each cluster instead of the whole population. The remaining individuals are indirectly evaluated. The aim is to maintain reasonable pop...

Hybrid Genetic Algorithms: A Review

Hybrid genetic algorithms have received significant interest in recent years and are being increasingly used to solve real-world problems. A genetic algorithm is able to incorporate other techniques within its framework to produce a hybrid that reaps the best from the combination. In this paper, different forms of integration between genetic algorithms and other search and optimization techniqu...

Quad Search and Hybrid Genetic Algorithms

A bit climber using a Gray encoding is guaranteed to converge to a global optimum in fewer than evaluations on unimodal 1-D functions and on multi-dimensional sphere functions, where bits are used to encode the function domain. Exploiting these ideas, we have constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1-D functions in not more than functio...