... learning processes. 26 01 2016 19 06 2016 13 09 2016 © Emerald Group Publishing Limited 2016 Emerald Group Publishing Limited Licensed re-use rights only Clustering Evolutionary algorithm Data mining Evolutionary computation Custer analysis Large amounts...

..., …, v_i+h are the locations (both pickup and delivery) associated with each job j of J_i that minimizes the following expression: (Equation 2) © Emerald Group Publishing Limited 2016 Genetic algorithms Evolutionary computation...

... a brief overview of the topic. Diploidy in evolutionary computation is a method designed to maintain the diversity in a population of individuals. Other methods include hypermutation (Cobb, 1990), adaptive mutation (Tinós and de Carvalho, 2004), injection of external genetic material into the population...

... with the other exams; largest weighted degree: the same as largest degree but weighted by the number of students involved; largest enrolment: decreasing by the number of enrolments for the exam; and random ordering: select exams randomly. Evolutionary computation Examination timetabling problem...

... Artificial immune system Fuzzy logic Knowledge acquisition Function approximation System identification Evolutionary computation By a rapid growth in the quality of high-performance computational tools, the introduction of emerging software and hardware technologies, global competitions...

... frameworks (and also mixture of experts) is another demanding objective which has instigated researchers of the computational intelligence society to take advantage of swarm and evolutionary computing (Liu et al., 2000). As we know, ensemble identifiers are really beneficial systems which help...

... corresponds to RI that utilizes a population size of 100 for solving the extreme problem instance X2. © Emerald Group Publishing Limited 2014 Evolutionary computation Multi-objective optimization Genetic algorithms Response surface methodology Dynamic environments Resource...

... for solving dynamic scheduling with time-varying number of tasks – Part I ”, Proceedings of the 2011 IEEE Congress on Evolutionary Computation, pp. 1703-1710. Abello, M.B. and Michalewicz, Z. (2014), “ Multi-objective resource-constrained...

... et al., 2010a, b ; Duan and Li, 2014): (Equation 1) © Emerald Group Publishing Limited 2014 Evolutionary computation Robotics Population-based swarm intelligence algorithms have been widely accepted and successfully applied to solve many optimization problems. Unlike...

...Yuji Sato; Mikiko Sato Purpose – The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units (GPUs...

... the competition. © Emerald Group Publishing Limited 2013 Evolutionary computation Genetic algorithms In the simplest case, an optimization problem consists of maximizing the enterprise profit and fully utilizing the limited production resources or minimizing the enterprise cost as a real...

... of the search space and, as a consequence, a better performance when compared to the traditional Gaussian mutation. © Emerald Group Publishing Limited 2013 Artificial immune system Evolutionary computation Self-adjusting systems Artificial immune systems (AISs) are population-based meta...