The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and nite time behavior of evolutionary algorithms with nite search spaces and discrete time scale. Results on evolutionary algorithms beyond nite space and discrete time are also presented but with reduced elaboration.

One application of evolutionary computation is developing agents which play in team-based games. In such games, it is often beneficial for each agent in a team to specialise in a different role within the team. We present several methods of evolving such specialisation by assessing agents as members of teams, and compare their performance in evolving solutions to a simulation of an immune syste...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

in this context, a novel structure has been proposed for simple differential evolutionary (de) algorithm to solve optimal recloser placement. for this, an operator is added to de algorithm to adapt concept of the problem. other contribution of this work is formulating a novel objective function. the proposed objective function has been formulated to improve four reliability indices which consis...

due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. in this study, three new evolutionary-based techniques, including evolutionary polynomial regression (epr), classical genetic p...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

in this paper, we study the problem of integrated capacitated hub location problem and seat inventory control considering concept and techniques of revenue management. we consider an airline company maximizes its revenue by utilizing the best network topology and providing proper booking limits for all itineraries and fare classes. the transportation system arises in the form of a star/star net...

this paper presents a multi-objective resource-constrained project scheduling problem with positive and negative cash flows. the net present value (npv) maximization and making span minimization are this study objectives. and since this problem is considered as complex optimization in np-hard context, we present a mathematical model for the given problem and solve three evolutionary algorithms;...

Evolutionary Computation has been successfully used to synthesise controllers for embodied agents and multi-agent systems in general. Notwithstanding this, continuous on-line adaptation by the means of evolutionary algorithms is still under-explored, especially outside the evolutionary robotics domain. In this paper, we present an on-line evolutionary programming algorithm that searches in the ...

The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: It has a potential of adjusting the algorithm to the problem while solving the problem. In this paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanis...