Fitness inheritance in genetic algorithms

Smith, Robert E.; Dike, B. A.; Stegmann, S. A.

doi:10.1145/315891.316014

Cited by 140 publications

(82 citation statements)

References 4 publications

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“…The idea of fitness inheritance was originally proposed by Smith et al (1995) [20]. It is an intuitive concept stating that the fitness of an individual is directly derived from that of its parents, and it discussed two types of inheritance: averaged inheritance and weighted inheritance.…”

Section: A Fitness Estimation Strategy Based On Fitness Inheritancementioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

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Section: A Fitness Estimation Strategy Based On Fitness Inheritancementioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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“…First proposed in (Smith et al, 1995), the fitness inheritance surrogate model has been applied in several problems (Bui et al, 2005;Ducheyne et al, 2003;Salami & Hendtlass, 2003;Sastry et al, 2004;Zheng et al, 1997) and algorithms (Pilato et al, 2008;Reyes-Sierra & Coello, 2005). In this method, all the individuals in the initial population have their fitness value calculated by the exact objective function evaluator.…”

Section: Fitness Inheritancementioning

confidence: 99%

Surrogate-Assisted Artificial Immune Systems for Expensive Optimization Problems

Bernardino¹,

Goliatt²,

Barbosa³

et al. 2009

Evolutionary Computation

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“…Ong et al [14,15] combined an evolutionary algorithm with a sequential quadratic programming solver in the spirit of Lamarckian learning, in which the trustregion method for interleaving exact models for the objective and constraint functions with computationally cheap surrogate models during local search was employed. Fitness inheritance, which was first proposed by Smith et al [16], can be seen as a special local surrogate technique, where the fitness of the individual is inherited from its parents or other individuals. Fonseca et al [17] introduced three inheritance surrogate models in genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

A two-layer surrogate-assisted particle swarm optimization algorithm

et al. 2014

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Like most Evolutionary Algorithms (EAs), Particle Swarm Optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. This paper proposes a two-layer surrogateassisted PSO (TLSAPSO) algorithm, in which a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original multimodal fitness function and guide the swarm to fly quickly to an optimum. In the meantime, a local surrogate model constructed using the data samples near the particle is built to achieve a fitness estimation as accurate as possible. The contribution of each surrogate in the search is empirically verified by experiments on uni-and multi-modal problems. The performance of the proposed TLSAPSO algorithm is examined on ten widely used benchmark problems, and the experimental results show that the proposed algorithm is effective and highly competitive with the state-of-the-art, especially for multimodal optimization problems.

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Fitness inheritance in genetic algorithms

Cited by 140 publications

References 4 publications

Efficient Query for Historical Data in Evolutionary Algorithm

Efficient Query for Historical Data in Evolutionary Algorithm

Surrogate-Assisted Artificial Immune Systems for Expensive Optimization Problems

A two-layer surrogate-assisted particle swarm optimization algorithm

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