In this paper, we argue that the original self-adaptation mechanism of the Evolution Strategies is useful by itself to handle constraints in global optimization. We show how using just three simple comparison criteria the simple Evolution Strategy can be led to the feasible region of the search space and find the global optimum solution (or a very good approximation of it). Different Evolution ...

Constructing genetic regulatory networks from expression data is one of the most important issues in systems biology research. However, building regulatory models manually is a tedious task, especially when the number of genes involved increases with the complexity of regulation. To automate the procedure of network construction, we develop a methodology to infer S-systems as regulatory systems...

1 Abstract— With the increasing number of intrusions in systems' and networks' infrastructures, Intrusion Detection Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of Evolutionary Algorithms in IDS has proved its maturity over the times. Although most of the research works have been based on the use of genetic ...

In this paper a comparison of the most recent algorithms for Multiobjective Optimization is realized. For this comparison are used the followings algorithms: Strength Pareto Evolutionary Algorithm (SPEA), Pareto Archived Evolution Strategy (PAES), Nondominated Sorting Genetic Algorithm (NSGA II), Adaptive Pareto Algorithm (APA). The comparison is made by using five test functions.

In many applications of constrained continuous black box optimization, the evaluation of fitness and feasibility is expensive. Hence, the objective of reducing the constraint function calls remains a challenging research topic. In the past, various surrogate models have been proposed to solve this issue. In this paper, a local surrogate model of feasibility for a self-adaptive evolution strateg...

The evolution of antimicrobial resistance often occurs in a variable environment, as antimicrobial is given periodically to a patient or added and removed from a medium. This environmental variability has a huge impact on the microorganisms' fitness landscape, and thus on the evolution of resistance. Indeed, mutations conferring resistance often carry a fitness cost in the absence of antimicrob...

This paper presents various Metamodel–Assisted Evolution Strategies which reduce the computational cost of optimisation problems involving time–consuming function evaluations. The metamodel is built using previously evaluated solutions in the search space and utilized to predict the fitness of new candidate solutions. In addition to previous works by the authors, the new metamodel takes also in...

Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, th...

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaini...

Evolutionary algorithms have been applied with great success to the difficult field of multi-objective optimisation. Nevertheless, the need for improvements in this field is still strong. We present a new evolutionary algorithm, ESP (the Evolution Strategy with Probabilistic mutation). ESP extends traditional evolution strategies in two principal ways: it applies mutation probabilistically in a...