Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover ...
We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The method provides an information theoretic m...
In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies ...
Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new...
State-of-the-art technologies in very large scale integration (VLSI) allow for the realization of gates with varying energy consumptions and hence delays (i.e., processing speeds) ...
This paper aims to forecast the economic impacts of changing land-use in UK uplands. We assume that farmers adaptively learn and respond to a dynamic economic environment. The main...
Nanlin Jin, Mette Termansen, Klaus Hubacek, Joseph...
This paper discusses a simple representation of variable-dimensional optimization problems for evolutionary algorithms. Although it was successfully applied to the optimization of ...
This paper studies the influence of what are recognized as key issues in evolutionary multi-objective optimization: archiving (to keep track of the current non-dominated solutions...
In this paper we describe a multi-objective problem solving approach, simultaneously minimizing average surface roughness Ra and build Time T, for object manufacturing by Rapid Pr...
High–dimensional optimization problems appear very often in demanding applications. Although evolutionary algorithms constitute a valuable tool for solving such problems, their ...