Evolutionary algorithm (EA) has become popular in global optimization with applications widely used in many industrial areas. However, there exists probable premature convergence problem when rugged contour situation is encountered. As to the original genetic algorithm (GA), no matter single population or multi-population cases, the ways to prevent the problem of probable premature convergence ...

The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an EA will often become trapped in a suboptimal solution(premature convergence). The ScoutingInspired Evolutionary Algorithm(SEA) is a relatively new technique that avoids premature convergence by determining whether a su...

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of individuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become t...

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [3, 4, 8]. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolut...

In this paper, a concept of degree of population diversity is introduced to quantitatively characterize and theoretically analyze the problem of premature convergence in genetic algorithms (GAs) within the framework of Markov chain. Under the assumption that the mutation probability is zero, the search ability of GA is discussed. It is proved that the degree of population diversity converges to...

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, humansimulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the la...

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [3, 4, 8]. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolut...

#1 Dean (R&D), Chairman & Professor (CSE/IT/MCA), H.C.T.M., Kaithal (Haryana), India #2 Professor, Deptt. of Computer Science & Applications, K.U., Kurukshetra (Haryana), India #3 Assistant Professor, Deptt. of Computer Science & Applications, K.U., Kurukshetra (Haryana), India __________________________________________________________________________________________________ AbstractThe use of ...

This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The applications of GA cover wide domains such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, etc. Fitness function is the measure of GA, distributed randomly in the population. Typically, th...

The capability of multi-objective evolutionary algorithms (MOEAs) to handle premature convergence is critically important when applied to realworld problems. Their highly multi-modal and discrete search space often makes the required performance out of reach to current MOEAs. Examining the fundamental cause of premature convergence in evolutionary search has led to proposing of a generic framew...