Statistical properties analysis of real world tournament selection in genetic algorithms
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Tournament selection
Benchmark (surveying)
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Fitness proportionate selection
We perform an experimental study about the effect of the tournament size parameter from the Tournament Selection operator. Tournament Selection is a classic operator for Genetic Algorithms and Genetic Programming. It is simple to implement and has only one control parameter, the tournament size. Even though it is commonly used, most practitioners still rely on rules of thumb when choosing the tournament size. For example, almost all works in the past 15 years use a value of 2 for the tournament size, with little reasoning behind that choice. To understand the role of the tournament size, we run a real-valued GA on 24 BBOB problems with 10, 20 and 40 dimensions. We also vary the crossover operator and the generational policy of the GA. For each combination of the above factors we observe how the quality of the final solution changes with the tournament size. Our findings do not support the indiscriminate use of tournament size 2, and recommend a more careful set up of this parameter.
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The roulette wheel selection strategy and tournament selection strategy in genetic algorithm(GA) are taken as examples and their performance is investigated on 13 benchmark functions.The performance of different selection strategies are compared and analyzed.Experimental results show that tournament selection strategy is more general than roulette wheel selection strategy, and also with better performance.Further experiments on tournament selection strategy show that a group scale with 60% to 80% of the population size performs better.These results give the useful guideline to design more efficient selection strategy.
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One of the defining operations of genetic algorithms is selection: choosing chromesomes from the population to generate offspring via crossover or mutation. Researchers have described many selection algorithms, including schemes that apply probabilities based on chromosomes' ranks in the population and that simulate tournaments among chromosomes. The paper investigates two rank based assignments of probabilities: linear normalization and exponential normalization, and two tournament selection schemes: 2-tournament selection without replacement and k-tournament selection with replacement. It makes explicit the probabilities that each associates with the population's chromosomes; demonstrates, following other researchers but using elementary arguments based on these probabilities, the equivalence of linear normalization with 2-tournament selection and of exponential normalization with k-tournament selection; and argues for the use of tournament selection rather than the explicit assignment of rank based probabilities whenever possible.
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Tournament selection is one of the most popular selection operators in Genetic Algorithms. Recently, its popularity is increasing because this operator is well suited for Parallel Genetic Algorithms applications. In this paper, new selection operator is proposed. The new operator, which should be an improvement of the tournament selection, is named ``Fine-grained Tournament Selection'' (FGTS). It is shown that classical tournament selection is a special case of the FGTS and that new operator preserves its good features. Furthermore, theoretical estimations for the FGTS are made. Estimations for the FGTS are similar to those for the classical tournament selection. Finally, classical tournament selection, rank-based selection and FGTS are experimentally compared on a real world NP-hard problem and the obtained results are discussed.
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Truncation selection
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This paper aims to propose a new selection procedure for real value encoding problem, specifically for shrimp diet problem.This new selection is a hybrid between two well-known selection procedure; roulette wheel selection and binary tournament selection.Shrimp diet problem is investigated to understand the hard constraints and the soft constraints involved.The comparison between other existing selections is also described for evaluation purposes.The result shows that roulette-tournament selection is better in terms of number of feasible solutions achieved and thus suitable for real value encoding problem.However, the combination with other crossover or mutation might be investigated to find the most suited combination that can obtain better best so far solution.
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Traveling Salesman Problem (TSP) is a matter of determining the order of several cities that must be passed by salesmen. One algorithm in solving TSP is Genetic Algorithm, which has 3 (three) main operators, namely selection, crossover, and mutation. In genetic algorithms, cities are represented as genes, while trips are represented as individuals. Selection is one of the main operators in genetic algorithms and one of the selection operator techniques is tournament selection. Tournament selection compares a number of individuals through “tournament” to choose the best individual based on fitness value, so that individuals will be selected to continue to the next generation. Modification were made in the way of competing individuals to determine the effect of tournament selection on genetic algorithms in TSP. In this study modifications were made to the way of individual tournament, namely first modification tournament selection and second modification tournament selection. The purpose of this study was to determine the effect of tournament modification on tournament selection on TSP. The results of this study are the secondmodification tournament selection gets average best fitness higher than the first modification tournament selection, so using the second modification tournament selection gets a shorter total distance compared to the first modification tournament selection.
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Fitness proportionate selection
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This paper presents the comparison of performance on a simple genetic algorithm (SGA) using roulette wheel selection and tournament selection. A SGA is mainly composed of three genetic operations, which are selection, crossover and mutation. With the same crossover and mutation operation, the simulation results are studied by comparing different selection strategies which are discussed in this paper. Qualitative analysis of the selection strategies is depicted, and the numerical experiments show that SGA with tournament selection strategy converges much faster than roulette wheel selection
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