Evolutionary combinatorial optimization (ECO) is a branch of evolutionary computing (EC) focused on finding optimal values for combinatorial problems. Algorithms ranging in this category require that the user defines, before the process of evolution, the fitness measure (i.e., the evaluation function) that will be used to guide the evolution of candidate solutions. However, there are many probl...

Evolutionary computation is a rapidly growing field during the last decade, althougth his origins can be traced back to the late 1950’s. A subset of evolutionary computation are evolutionary algorithms (EA). This paper suplies a brief survey of their earlier beginnings, design and different implementations like genetic algorithms(GA), evolutionary strategies(ES) and evolutionary programming(EP)...

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffl...

As a searching algorithm of global optimization, evolutionary computation has become a key technology of intelligent computation. This paper mainly discusses the present status and future developments in the applications of evolutionary computation in filtering information on web pages, including texts, images, audios and videos, etc. It is necessary to note that combining evolutionary computat...

RFreak is an R package providing a framework for evolutionary computation. By enwrapping the functionality of an evolutionary algorithm kit written in Java, it offers an easy way to do evolutionary computation in R. In addition, application examples where an evolutionary approach is promising in computational statistics are included and described in this paper. The package is thus further suppo...

Coevolution is a powerful tool in evoltionary computing that mitigate some of its endemic problems, namely stagnation in local optima and lack of convergence in high dimensionality problems. Since its inception in 1990, there are multiple articles that have contributed greatly to the development and improvement of the coevolutionary techniques. In this report we review some of those landmark ar...

This paper presents some ideas about an approach which aims at extending existing methodologies for functional testing. Experience in automotive applications has shown that when selecting parameters for functional testing, many times a tester has equivalence classes in mind. Instead of losing valuable information in the process, support should be given to make them manageable. The proposed appr...

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algo...

Swarm and evolutionary computation (SEC) [...]

One of the roots of evolutionary computation was the idea of Turing about unorganized machines. The goal of this paper is the development of foundations for evolutionary computations, connecting Turing’s ideas and the contemporary state of art in evolutionary computations. The theory of computation is based on mathematical models of computing automata, such as Turing machines or finite automata...