Estimating the Evolution Direction of Populations to Improve Genetic Algorithms

Meta-heuristics have been successfully used to solve a wide variety of problems. However, one issue many techniques have is their risk of being trapped into local optima, or to create a limited variety of solutions (problem known as "population drift"). During recent and past years, different kinds of techniques have been proposed to deal with population drift, for example hybridizing genetic algorithms with local search techniques or using niche techniques. This paper proposes a technique, based on Singular Value Decomposition (SVD), to enhance Genetic Algorithms (GAs) population diversity. SVD helps to estimate the evolution direction and drive next generations towards orthogonal dimensions. The proposed SVD-based GA has been evaluated on 11 benchmark problems and compared with a simple GA and a GA with a distance-crowding schema. Results indicate that SVD-based GA achieves significantly better solutions and exhibits a quicker convergence than the alternative techniques.