Penalty Function Methods for Constrained Optimization with Genetic Algorithms
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Cites background from "Penalty Function Methods for Constr..."
...However, in a constrained problem, it needs to use additional functions and methods which keep solutions in feasible regions [7], [15-18]....
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Cites methods from "Penalty Function Methods for Constr..."
...These approaches can be grouped in four major categories [1, 2]: (1) methods based on penalty functions that are also known as indirect constraint handling, (2) methods based on a search of feasible solutions including repairing unfeasible individuals [3, 4], superiority of feasible points [5], and behavioral memory [6], (3) methods based on preserving feasibility of solutions like preserving feasibility by designing special crossover and mutation operators [7], the GENOCOP system [8], searching the boundary of feasible region [9], and homomorphous mapping [10], and (4) Hybrid methods [11–13]....
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...In [2], a survey has been performed on several types of these methods including death penalty [2, 15], static penalty [16, 17], dynamic penalty [18, 19], annealing penalty [20, 21], adaptive penalty [22–24], segregated GA [25], and coevolutionary penalty [26]....
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"Penalty Function Methods for Constr..." refers background in this paper
...These approaches can be grouped in four major categories [28]: Category 1: Methods based on penalty functions - Death Penalty [2] - Static Penalties [15,20] - Dynamic Penalties [16,17] - Annealing Penalties [5,24] - Adaptive Penalties [10,12,35,37] - Segregated GA [21] - Co-evolutionary Penalties [8] Category 2: Methods based on a search of feasible solutions - Repairing unfeasible individuals [27] - Superiority of feasible points [9,32] - Behavioral memory [34]...
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