Titlebook: Simulated Evolution and Learning; 7th International Co Xiaodong Li,Michael Kirley,Yuhui Shi Conference proceedings 2008 Springer-Verlag Ber

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intelligible

A PSO Based Adaboost Approach to Object Detectioneriment results show that both approaches perform quite well for the pasta detection problem, and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only for this problem.

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Adaptive Non-uniform Distribution of Quantum Particles in mQSO To obtain the requested circumstances in the testing environment the number of sub-swarms is fixed. The results show high efficiency and robustness of the proposed method in all of the tested variants.

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Improving XCS Performance by Distributionlti-agent scenario is used to evaluate and compare different distributed LCS variants. Results show that improvements in learning speed can be achieved by cleverly dividing a problem into smaller learning sub-problems.

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Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problemnto multiprocessor system graph where the program completion time is minimized. The problem is know to be a NP-complete problem. In this paper we show that GEO is to able to solve this problem with better performance than genetic algorithm.

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0302-9743 r 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and

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Modelling Behaviour Cycles for Life-Long Learning in Motivated Agentses to learn, adapt and evolve in complex, dynamic environments. This paper introduces a model of behaviour cycles for artificial systems. This model provides a new way to conceptualise and evaluate life-long learning in artificial agents. The model is demonstrated for evaluating the sensitivity of m

Bureaucracy

Breaking the Synaptic Dogma: Evolving a Neuro-inspired Developmental Networkg is seen as the process of obtaining the strengths of connections between neurons (i.e. weights). We refer to this as the ’synaptic dogma’. This is in marked contrast with biological networks which have time dependent morphology and in which practically all neural aspects can change or be shaped by

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