Differential Evolution with Reversible Linear Transformations
This is an incremental improvement for evolutionary algorithm users, addressing diversity maintenance in differential evolution.
The paper tackled the problem of small populations and rapid diversity loss in differential evolution by proposing a method that uses reversible linear transformations on solution triplets to generate new candidates without fitness evaluation, and it outperformed vanilla DE and a triple-mutation variant on benchmark functions, gene repressilator parameter discovery, and neural network learning.
Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.