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Intelligent Computing Theories: 9th International by Yu Zhang (auth.), De-Shuang Huang, Vitoantonio Bevilacqua,

By Yu Zhang (auth.), De-Shuang Huang, Vitoantonio Bevilacqua, Juan Carlos Figueroa, Prashan Premaratne (eds.)

This e-book constitutes the refereed convention complaints of the ninth foreign convention on clever Computing, ICIC 2013, held in Nanning, China, in July 2013. The seventy four revised complete papers awarded have been conscientiously reviewed and chosen from a variety of submissions and are geared up in topical sections on neural networks, nature encouraged computing and optimization, cognitive technology and computational neuroscience, wisdom discovery and knowledge mining, evolutionary studying and genetic algorithms computer studying idea and techniques, normal language processing and computational linguistics, fuzzy concept and types, tender computing, unsupervised and strengthened studying, clever computing in finance, clever computing in petri nets, clever information fusion and knowledge safeguard, digital truth and laptop interplay, clever computing in development attractiveness, clever computing in photograph processing, clever computing in robotics, complicated structures conception and methods.

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Additional info for Intelligent Computing Theories: 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings

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2 Problem Description The problem size is denoted by n × m × L where n is the number of jobs, m is the number of machines, and L means the repeat or reentrant times. -C. Li et al. usually defined as follows. m . In this case, every job can be decomposed into several layers and each layer starts on 1 and finishes on. That is, each job should visits every machine L (L>1) times. The job ordering is the same on all machines. No preemption is allowed. Once an operation is started, it must be completed without any interruption.

3, and calculate the makespan of each individual and update π lbest ( gen) . 7. 8 to π lbest ( gen) and update π gbest . 2. Step 8: If gen < genMax , then go to Step2. Step 9: Output π gbest . It can be seen from the above procedure that Step 7 uses π gbest obtained in Step 6 and the self-adaptive α ( gen − 1) obtained in Step 3 to update the probability matrix, which means new generated individuals can aptly absorb the information of the global best individual during the evolution process and then guide the search to more promising regions, and Step 5 is the perturbation operator, which can restrain the search from dropping into local optima and drive the search to quite different regions.

2: If f (π i 2 (0)) < f (π i1 (0)) , then set π i1 (0) = π i 2 (0) . 3: LOOP = LOOP + 1 . 1. Step 3: If f (π i1 (0)) < f (π i (0)) , then set π i (0) = π i1 (0) . Step 4: Set i=i+1. If i ≤ PS, then goto Step 1. Step 5: Output the final pop(0) . 5 Probability Matrix Training The population initialization strategy in the above subsection can guarantee the initial population has a better distribution in the solution space. However, based on our previous tests, the PBIL algorithm with the proposed population initialization strategy was still likely to fall into low-quality local optima at the start phase.

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