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Boosting genetic algorithms with self-adaptive selection
- In Proceedings of the IEEE Congress on Evolutionary Computation
, 2006
"... Abstract — In this paper we evaluate a new approach to selection in Genetic Algorithms (GAs). The basis of our approach is that the selection pressure is not a superimposed parameter defined by the user or some Boltzmann mechanism. Rather, it is an aggregated parameter that is determined collectivel ..."
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Cited by 9 (1 self)
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Abstract — In this paper we evaluate a new approach to selection in Genetic Algorithms (GAs). The basis of our approach is that the selection pressure is not a superimposed parameter defined by the user or some Boltzmann mechanism. Rather, it is an aggregated parameter that is determined collectively by the individuals in the population. We implement this idea in two different ways and experimentally evaluate the resulting genetic algorithms on a range of fitness landscapes. We observe that this new style of selection can lead to 30-40 % performance increase in terms of speed. I.
Information theoretic justification of Boltzmann selection and its generalization to Tsallis case
- Proceedings of IEEE Congress on Evolutionary Computation
, 2005
"... Abstract- A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of Boltzmann distribution, to weigh the configurations in the selection mechanism. This generalization is motiv ..."
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Cited by 3 (2 self)
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Abstract- A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of Boltzmann distribution, to weigh the configurations in the selection mechanism. This generalization is motivated by the recently proposed generalized simulated annealing algorithm based on Tsallis statistics. We also present an information theoretic justification to use Boltzmann distribution in the selection mechanism, since these ‘canonical ’ distributions have deep roots in information theory. Our simulation results show that for an appropriate choice of nonextensive index that is offered by Tsallis statistics, evolutionary algorithms based on this generalization outperform algorithms based on Boltzmann distribution. 1
DISCRETE OPTIMIZATION VIA APPROXIMATE ANNEALING ADAPTIVE SEARCH WITH STOCHASTIC AVERAGING
"... We propose a random search algorithm for black-box optimization with discrete decision variables. The algorithm is based on the recently introduced Model-based Annealing Random Search (MARS) for global optimization, which samples candidate solutions from a sequence of iteratively focusing distributi ..."
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We propose a random search algorithm for black-box optimization with discrete decision variables. The algorithm is based on the recently introduced Model-based Annealing Random Search (MARS) for global optimization, which samples candidate solutions from a sequence of iteratively focusing distribution functions over the solution space. In contrast with MARS, which requires a sample size (number of candidate solutions) that grows at least polynomially with the number of iterations for convergence, our approach employs a stochastic averaging idea and uses only a small constant number of candidate solutions per iteration. We establish global convergence of the proposed algorithm and provide numerical examples to illustrate its performance. 1
Boosting Genetic Algorithms with Self-Adaptive Selection
"... Abstract — In this paper we evaluate a new approach to selection ..."
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Institute of Astronautics,
"... Product design and development is recognized by many firms as a crucial activity. The ability to quickly introduce new products into the market is a key factor for determining corporate health and profitability �1�. As a result, design management researchers ..."
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Product design and development is recognized by many firms as a crucial activity. The ability to quickly introduce new products into the market is a key factor for determining corporate health and profitability �1�. As a result, design management researchers
its generalization to Tsallis case
"... Information theoretic justification ofBoltzmann selection and ..."
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