Entropy-based genetic algorithm for solving TSP

Tsujimura, Y.; Gen, Mitsuo

doi:10.1109/kes.1998.725924

Cited by 44 publications

(28 citation statements)

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“…For example, Farhang-Mehr and Azarm [12] introduce an entropy-based multi-objective genetic algorithm. Tsujimura and Gen [42] calculate the entropy of each gene for the individuals in the population and compare it with a threshold value; when the number of genes whose entropy is lower than the threshold is greater than a given amount, the diversity of the population is increased by a process of allele exchange. Finally, Liu, Mernik, and Bryant [24] calculate entropy from the fitness distribution in the population and the entropy value is employed to change the mutation probability: When the entropy is greater than 0.5, the mutation probability is decreased to facilitate exploitation.…”

Section: Diversity-adaptive Controlmentioning

confidence: 99%

Adaptive generalized crowding for genetic algorithms

Mengshoel

Galán

Dios

2014

Information Sciences

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The genetic algorithm technique known as crowding preserves population diversity by pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will survive (replacement phase). The replacement phase of crowding is usually carried out through deterministic or probabilistic crowding, which have the limitations that they apply the same selective pressure regardless of the problem being solved and the stage of genetic algorithm search. The recently developed generalized crowding approach introduces a scaling factor in the replacement phase, thus generalizing and potentially overcoming the limitations of both deterministic and probabilistic crowding. A key problem not previously addressed, however, is how the scaling factor should be adapted during the search process in order to effectively obtain optimal or near-optimal solutions. The present work investigates this problem by developing and evaluating two methods for adapting, during search, the scaling factor. We call these two methods diversity-adaptive and self-adaptive generalized crowding respectively. Whereas the former method adapts the scaling factor according to the population's diversity, the latter method includes the scaling factor in the chromosome for self-adaptation. Our experiments with real function optimization, Bayesian network inference, and the Traveling Salesman Problem show that both diversity-adaptive and self-adaptive generalized crowding are consistent techniques that produce strong results, often outperforming traditional generalized crowding.

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Section: Diversity-adaptive Controlmentioning

confidence: 99%

Adaptive generalized crowding for genetic algorithms

Mengshoel

Galán

Dios

2014

Information Sciences

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“…Our bodies are under constant attack by antigens that can stimulate the adaptive immune system. Antigens might be foreign, such as surface molecules present on pathogens, or selfantigens, which are composed of cells or molecules of our own bodies [53]. Immune algorithm considers the objective functions and their associated constraints as antigens, which are to be identified by the antibodies, and the solutions which play the rules of antibodies.…”

Section: V8 Body Immune Algorithmmentioning

confidence: 99%

Optimal Capacitor Placement Techniques in Transmission and Distribution Networks to Reduce Line Losses and Voltage Stability Enhancement: A Review

Mahela¹,

Mittal²,

Goyal³

2012

IOSRJEEE

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“…With the data structure of genes in Fig. 3, the entropy of the th gene is defined as [15] ( 4) where is the quantity of antibodies, and is the probability that the th allele comes out at the th gene. If all alleles at the th gene are the same, the entropy of the th becomes zero.…”

Section: A Diversitymentioning

confidence: 99%

Optimal Placement of Line Switches for Distribution Automation Systems Using Immune Algorithm

Chen

Lin

Chuang

et al. 2006

IEEE Trans. Power Syst.

157

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Abstract-To enhance the cost effectiveness of the distribution automation system (DAS), this paper proposes the immune algorithm (IA) to derive the optimal placement of switching devices by minimizing the total cost of customer service outage and investment cost of line switches. The reliability index of each service zone defined by the boundary switches is derived to solve the expected energy not served due to fault contingency, and the customer interruption cost is then determined according to the customer type and power consumption within the service zone. To demonstrate the effectiveness of proposed IA methodology to solve the optimal placement of line switches, a practical distribution system of Taiwan Power Company (Taipower) is selected for computer simulation to explore the cost benefit of line switch placement for DAS.Index Terms-Distribution automation system (DAS), immune algorithm (IA), outage management system (OMS).

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Entropy-based genetic algorithm for solving TSP

Cited by 44 publications

References 1 publication

Adaptive generalized crowding for genetic algorithms

Adaptive generalized crowding for genetic algorithms

Optimal Capacitor Placement Techniques in Transmission and Distribution Networks to Reduce Line Losses and Voltage Stability Enhancement: A Review

Optimal Placement of Line Switches for Distribution Automation Systems Using Immune Algorithm

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