Genetic and Evolutionary Computation Conference 

About: Genetic and Evolutionary Computation Conference is an academic conference. The conference publishes majorly in the area(s): Evolutionary algorithm & Genetic algorithm. Over the lifetime, 9012 publications have been published by the conference receiving 150985 citations.

On the practical genetic algorithms

Abstract: This paper offers practical design-guidelines for developing efficient genetic algorithms (GAs) to successfully solve real-world problems. As an important design component, a practical population-sizing model is presented and verified.

BOA: the Bayesian optimization algorithm

Abstract: In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. To estimate the distribution, techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identifies, reproduces and mixes building blocks up to a specified order. It is independent of the ordering of the variables in the strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, prior information is not essential. Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.

Evolutionary multi-criterion optimization

Abstract: GECCO-2010 Tutorial on EMO Portland, USA (8 July'10) 2 Kalyanmoy Deb holds Deva Raj Chair Professor at Indian Institute of Technology Kanpur in India. He is the recipient of the MCDM Edgeworth-Pareto award by the Multiple Criterion Decision Making (MCDM) Society. He has also received Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005 from Govt. of India. He has also received the `Thomson Citation Laureate Award' for having highest number of citations in Computer Science during the past ten years in India. He is a fellow of Indian National Academy of Engineering (INAE), Indian National Academy of Sciences, and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003. He has written more than 240 international journal and conference research papers. More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm

PESA-II: region-based selection in evolutionary multiobjective optimization

Abstract: We describe a new selection technique for evolutionary multiobjective optimization algorithms in which the unit of selection is a hyperbox in objective space. In this technique, instead of assigning a selective fitness to an individual, selective fitness is assigned to the hyperboxes in objective space which are currently occupied by at least one individual in the current approximation to the Pareto frontier. A hyperbox is thereby selected, and the resulting selected individual is randomly chosen from this hyperbox. This method of selection is shown to be more sensitive to ensuring a good spread of development along the Pareto frontier than individual-based selection. The method is implemented in a modern multiobjective evolutionary algorithm, and performance is tested by using Deb's test suite of `T' functions with varying properties. The new selection technique is found to give significantly superior results to the other methods compared, namely PAES, PESA, and SPEA; each is a modern multi-objective optimization algorithm previously found to outperform earlier approaches on various problems.

An approach for QoS-aware service composition based on genetic algorithms

Abstract: Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (QoS)--aware composition and late--binding. This allows to bind, at run--time, a service--oriented system with a set of services that, among those providing the required features, meet some non--functional constraints, and optimize criteria such as the overall cost or response time. In other words, QoS--aware composition can be modeled as an optimization problem.We propose to adopt Genetic Algorithms to this aim. Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes. The paper describes our approach and its applicability, advantages and weaknesses, discussing results of some numerical simulations.