Recently, a new iterative optimization framework utilizing an evolutionary algorithm called ”Prototype Optimization with Evolved iMprovement Steps” (POEMS) was introduced, which showed good performance on hard optimization problems large instances of TSP and real-valued optimization problems. Especially, on discrete optimization problems such as the TSP the algorithm exhibited much better searc...

Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a populationbased metaheuristic inspired on the gravitational forces...

During the recent years, multiobjective evolutionary algorithms have matured as a flexible optimization tool which can be used in various areas of real-life applications. Practical experiences showed that typically the algorithms need an essential adaptation to the specific problem for a successful application. Considering these requirements, we discuss various issues of the design and applicat...

We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitne...

Although there are many versions of evolutionary algorithms that are tailored to multi–criteria optimization, theoretical results are apparently not yet available. Here, it is shown that results known from the theory of evolutionary algorithms in case of single criterion optimization do not carry over to the multi–criterion case. At first, three different step size rules are investigated numeri...

The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, is a combination of the recombination and mutation steps used in evolutionary algorithms. We introduce t...

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimizat...

Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evol...

Evolutionary algorithms incorporate principles from biological population genetics to perform search, optimization, and learning. This article discusses issues arising in the application of evolutionary algorithms to problems in robotics.

This work deals with an investigation on optimization of the feedback control of chaos based on using of the evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control. As a model of deterministic chaotic system the Henon map was used. The optimizations were realized in several ways, each one for another set of paramet...