DiverGene: Experiments on Controlling Population Diversity in Genetic Algorithm with a Dispersion Operator

Abstract

Abstract-We present diverGene -a novel, diversity-aware population selection operator for genetic algorithm -to be used especially for particularly complex and multi-criteria optimisation problems. Genetic algorithm is one of the most known evolutionary algorithms for solving hard optimisation problems. Many attempts have been made to improve its convergence rate and quality of the result. In this paper we propose a novel extension of the selection operator that makes it possible to control the level of diversity in the population. We discuss its theoretical background, including its computational hardness and propose an efficient way of computing it. The approach is implemented and tested on three hard optimisation problems: Knapsack Problem, Travelling Salesman Problem and a relatively new Travelling Thief Problem that might be viewed as the composition of the latter two. We report experimental results that seem to indicate that the novel approach has a potential to improve the quality of the results for some hard optimisation problems.

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

“…We ran each algorithm 10, 000 times on every instance of the eil76 series of the TTP benchmark suite [22]. We computed 100 (capped due to practical reasons) distinct tours by INV, 25 We then applied the DP to every tour produced by each of the algorithms. Figure 1 depicts the resulted rewards on some sample TTP instances, where each box with whiskers reports the distribution of the rewards for a certain instance and the corresponding algorithm.…”

Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia

“…Examples range from databases (Vee et al 2008) to Web search (Agrawal et al 2009) or to the quite novel problem of graphical entity summarisation in semantic knowledge graphs (Sydow et al 2013). A recent work (Strzezek et al 2015) demonstrates that a controlled level of population diversity increases the performance of genetic algorithm for some hard optimisation problems.…”

“…Recent research also indicates that diversity of population plays a positive role in evolutionary algorithms (Strzezek et al 2015) Our hypothesis studied in this article is that diversity of editors and teams is a factor that positively affects the quality of work in a virtual cooperative environments.…”