This paper surveys strategies applied to avoid premature convergence in Genetic
Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The …

Population diversity is crucial in evolutionary algorithms to enable global exploration and to
avoid poor performance due to premature convergence. This chapter reviews runtime …

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Population diversity is essential for avoiding premature convergence in genetic algorithms
(GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in …

Gravitational search algorithm is an effective population-based algorithm. It simulates the
law of gravity to implement the interaction among particles. Although it can effectively …

Explaining to what extent the real power of genetic algorithms (GAs) lies in the ability of
crossover to recombine individuals into higher quality solutions is an important problem in …

Automatic extracting of knowledge from massive data samples, ie, big data analytics (BDA),
has emerged as a vital task in almost all scientific research fields. The BDA problems are …

We extend the theory of non-elitist evolutionary algorithms (EAs) by considering the offspring
population size in the (1, λ) EA. We establish a sharp threshold at λ= log ee− 1 n≈ 5 log 10 …

Evolutionary algorithms (EA s) have given rise to many parallel variants, fuelled by the
rapidly increasing number of CPU cores and the ready availability of computation power …

Population diversity is essential for the effective use of any crossover operator. We compare
seven commonly used diversity mechanisms and prove rigorous run time bounds for the (μ+ …