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IEEE CEC 2015 features a world-class conference that aims to bring together researchers and practitioners in the field of Evolutionary Computation (EC) and computational intelligence from all around the globe. Technical exchanges within the research community will encompass keynote lectures, regular and special sessions, tutorials, competitions and workshops as well as poster presentations. In addition,...
Identifying the epistasis models between single nucleotide polymorphisms (SNPs) in several genes can explain the susceptibility to diseases. The statistical methods have been used to identify the significant epistasis models according to the related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high calculations limit the statistic to identify the...
The concept of evolutionary ontology has been introduced recently. They are actually genetic algorithms which use ontologies instead of mathematical structures, such as bit strings or numbers. An ontology has a significant expressivity power, therefore it allows more complex genetic operators, beside the classical ones. In this article we introduce several such new genetic operators (intersection...
A new variant of the Genetic Algorithm (GA) inspired by monogamy mating system is put forward. The Monogamous Pairs Genetic Algorithm (MopGA) incorporates two important operations: pair bonding and infidelity at a small probability. With pair bonding, parents continue to mate at each iteration until their bond expires. In the meantime, infidelity generates variety and promotes diversity via mating...
In this paper, the multi-agent genetic algorithm (MAGA) is combined with the variable neighborhood search (VNS) to solve resource investment project scheduling problems (RIPSPs). An agent, coded by a valid activity list and a capacity list, represents a candidate solution to the RIPSPs. All agents live in a lattice-like environment, with each agent fixed on a lattice point. To increase energies, a...
An adaptive genetic algorithm using mutation matrix is introduced for the solution of a series of zero/one knapsack problems of increasing complexity and structure. The evolution of the population in our adaptive genetic algorithm is based on a time dependent mutation matrix that is co-evolving, guided by the locus statistics and the fitness distribution of the population. This co-evolution of the...
In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information...
Intelligent constraint handling evolutionary algorithm (ICHEA) is a recently proposed variation of evolutionary algorithm (EA) that solves real-valued constraint satisfaction problems (CSPs) efficiently. Initially it was designed to solve CSPs only, however, it has been shown effective in solving static and dynamic constraint optimization problems as well (Sharma and Sharma, 2012). ICHEA has ability...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
The path planning task for mobile robots consists of define a trajectory to the robot leaves its start position and reach its goal without to collide with obstacles. In general, the robot needs to know previous information about the environment (e.g. maps, predefined routes) to plan its trajectory. In an exploration task, the robot does not know the environment and discovers it when moving to reach...
Grammatical Evolution (GE) is applied to the problem of load balancing in heterogeneous cellular network deployments (HetNets). HetNets are multi-tiered cellular networks for which load balancing is a scalable means to maximise network capacity, assuming similar traffic from all users. This paper describes a proof of concept study in which GE is used in a genetic algorithm-like way to evolve constants...
In present days, the road network in any major city faces the constant pressure of accommodating an ever increasing number of vehicles while conserving a congestion-free status. However, identifying key intersections that will soon become congested is a difficult task, performed by tedious, thorough simulations; even more difficult is to adapt the road network so as to increase its efficiency and...
Most of the practical optimization problems involve variables and parameters that are not reliable and often vary around their nominal values. If the optimization problem is solved at the nominal values without taking the uncertainty into account, it can lead to severe operational implications. In order to avoid consequences that can be detrimental for the system, one resorts to the robust optimization...
Cooperative coevolution has proven to be efficient in solving global optimisation and real world application problems. However, it is highly sensitive to problem decomposition, especially in the context of non-separable functions that possess interacting decision variables. Problem decomposition has been a challenge of cooperative coevolution. Efficient problem decomposition strategy ensures that...
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