This work describes the structure and the operation of a basic genetic algorithm. The studies show that the genetics algorithms (GAs) always offer an answer that tends to be the best over time, satisfied with knowledg...
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ISBN:
(纸本)9783319401621;9783319401614
This work describes the structure and the operation of a basic genetic algorithm. The studies show that the genetics algorithms (GAs) always offer an answer that tends to be the best over time, satisfied with knowledge on the problem, we can improve the function of evaluation that was always search of inside the current population those solutions that possess the best characteristic and tries to combine them of form to generate solutions still better and the process is repeated until we have obtained a solution for our problem. The (GA) go in the scene to resolve those problems whose exact algorithms are extremely slow or unable to obtain a solution.
The paper presents three intelligent algorithms, namely, basic genetic algorithm, Hopfield neural network and basic ant colony algorithm to solve the TSP problem. Then different algorithms are compared in the perspect...
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The paper presents three intelligent algorithms, namely, basic genetic algorithm, Hopfield neural network and basic ant colony algorithm to solve the TSP problem. Then different algorithms are compared in the perspectives of time complexity, space complexity, the advantages and disadvantages of the calculation results, and difficulty level of realization. We use the application of paired comparison matrix to make comprehensive evaluation, and then give the value of comprehensive evaluation in engineering.
It is difficult for basic genetic algorithm and evolutionary programming algorithm to converge at global optimal solution of real-continual function in practice, although both of the algorithms have the ability for ge...
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It is difficult for basic genetic algorithm and evolutionary programming algorithm to converge at global optimal solution of real-continual function in practice, although both of the algorithms have the ability for getting the global optimal solution with convergent probabilities 1 in theory. In this paper, a new hybrid geneticalgorithm based on clan competition is proposed, and it is proved that the probability of the new algorithm convegent to the global optimal solution is 1, Numerical experiments results illustrate that, compared with the former two algorithms, the new algorithm is the robustest among the three algorithms, what's more, it has the highest precision with the equal parameters.
In view of the slowness and the locality of convergence for basic genetic algorithm (BGA for short) in solving complex optimization problems, we proposed an improved geneticalgorithm named self-adpative and multi-pop...
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ISBN:
(纸本)9781424447053
In view of the slowness and the locality of convergence for basic genetic algorithm (BGA for short) in solving complex optimization problems, we proposed an improved geneticalgorithm named self-adpative and multi-population composite geneticalgorithm (SM-CGA for short), and gave the structure and implementation steps of the algorithm;then we consider its global convergence under the elitist preserving strategy using Markov chain theory, and analyze its performance through three examples from different aspects. All of the results indicate that the new algorithm possess interesting advantages such as better convergence, less chance trapping into premature states, so it can be widely used in many large-scale and high-accuracy optimization problems.
In order to improve basic genetic algorithm's premature convergence and random roam and verify improved algorithm's application effect in civil engineering, the article brings forward four improved measures, w...
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In order to improve basic genetic algorithm's premature convergence and random roam and verify improved algorithm's application effect in civil engineering, the article brings forward four improved measures, with which adaptive and improved geneticalgorithm came into being. By the use of the chaos serial's properties of 'ergodicity, randomness, regularity', original population is generated. Fitness index scaling is adopted. Adaptive crossover and mutation rate formula are presented. Crossover rate and mutation rate are adjusted by extent coefficient. Improved optimal result is compared with basic genetic algorithm. Evolution time and convergence precision are increased. By a numerical example of bar truss structure with unsymmetrical loads in civil engineering, adaptive and improved geneticalgorithm is feasible.
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