A Novel Diversity-Based Replacement Strategy for Evolutionary Algorithms

Abstract

Premature convergence is one of the best-known drawbacks that affects the performance of evolutionary algorithms. An alternative for dealing with this problem is to explicitly try to maintain proper diversity. In this paper, a new replacement strategy that preserves useful diversity is presented. The novelty of our method is that it combines the idea of transforming a single-objective problem into a multiobjective one, by considering diversity as an explicit objective, with the idea of adapting the balance induced between exploration and exploitation to the various optimization stages. Specifically, in the initial phases, larger amounts of diversity are accepted. The diversity measure considered in this paper is based on calculating distances to the closest surviving individual. Analyses with a multimodal function better justify the design decisions and provide greater insight into the working operation of the proposal. Computational results with a packing problem that was proposed in a popular contest illustrate the usefulness of the proposal. The new method significantly improves on the best results known to date for this problem and compares favorably against a large number of state-of-the-art schemes.

On the comparison of initialisation strategies in differential evolution for large scale optimisation

“…Since all experiments used stochastic algorithms, each execution was repeated num Rep = 3 × 10 3 times, with the aim of comparing the different initialisation strategies with enough statistical confidence. With respect to the former, comparisons were carried out by applying the following statistical analysis [14]. First, a Shapiro-Wilk test was performed to check whether the values of the results followed a normal (Gaussian) distribution or not.…”

“…Consequently, it was also considered for the lsgo competition organised during cec'15. 1 The set is composed of 15 functions ( f 1 -f 15 ) with different features: fully-separable functions (category 1: f 1 -f 3 ), partially additively separable functions (category 2: f 4 -f 11 ), overlapping functions (category 3: f 12 -f 14 ), and a non-separable function (category 4: f 15 ). Following the indications given for the different editions of the lsgo competition, in the current work, the number of decision variables D was fixed to 1000 for all the aforementioned functions, with the exception of problems f 13 and f 14 , where D was fixed to 905 decision variables due to overlapping subcomponents.…”

Finding Multiple Roots of Nonlinear Equation Systems via a Repulsion-Based Adaptive Differential Evolution

“…In evolutionary computation, the diversity of population plays a crucial role in balancing the exploitation and exploration [37], [38]. The diversity is also a major factor which affects the finding of multiple roots of NESs in a single run.…”

Hybridisation of Evolutionary Algorithms Through Hyper-heuristics for Global Continuous Optimisation

“…Since all experiments used stochastic algorithms, each execution was repeated 100 times with different initial seeds. With respect to the former, comparisons between algorithms were carried out by applying the following statistical analysis [18]. First, a Shapiro-Wilk test was performed to check whether the values of the results followed a normal (Gaussian) distribution.…”