A Self-adaptive Multipeak Artificial Immune Genetic Algorithm
نویسندگان
چکیده
منابع مشابه
A Self-adaptive Multipeak Artificial Immune Genetic Algorithm
Genetic algorithm is a global probability search algorithm developed by simulating the biological natural selection and genetic evolution mechanism and it has excellent global search ability, however, in practical applications, premature convergence occurs easily in the genetic algorithm. This paper proposes an self-adaptive multi-peak immune genetic algorithm (SMIGA) and this algorithm integra...
متن کاملSlope Stability Analysis Using a Self-Adaptive Genetic Algorithm
This paper introduces a methodology for soil slope stability analysis based on optimization, limit equilibrium principles and method of slices. In this study, the slope stability analysis problem is transformed into a constrained nonlinear optimization problem. To solve that, a Self-Adaptive Genetic Algorithm (GA) is utilized. In this study, the slope stability safety factors are the objective ...
متن کاملGenetic Algorithms A Self Adaptive Hybrid Genetic Algorithm
This paper presents a self-adaptive hybrid genetic algorithm (SAHGA) and compares its performance to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on two multi-modal test functions with complex geometry. The SAHGA is shown to be far more robust than the NAHGA, providing fast and reliable convergence across a broad range of parameter settings. For the mos...
متن کاملSelf-Adaptive Genetic Algorithm for Clustering
Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the al...
متن کاملAgent Oriented Self Adaptive Genetic Algorithm
Efficiency of Genetic Algorithms (GAs) depends largely on the parameters such as crossover rate and mutation rate. In general, however, it is difficult to adjust those parameters manually. Although there are a few researches about adaptive GAs for adjusting multiple parameters, they require extremely large computation costs. In this paper, we propose a new algorithm based on multi agent techniq...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TELKOMNIKA (Telecommunication Computing Electronics and Control)
سال: 2016
ISSN: 2302-9293,1693-6930
DOI: 10.12928/telkomnika.v14i2.2753