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Constructive Genetic Algorithm for Clustering Problems
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Genetic algorithms (GAs) have recently been accepted as powerful approaches to solving optimization problems. It is also well-accepted that building block construction (schemata formation and conservation) has a positive influence on GA behavior. Schemata are usually indirectly evaluated through a derived structure. We introduce a new approach called the Constructive Genetic Algorithm (CGA), which allows for schemata evaluation and the provision of other new features to the GA. Problems are modeled as bi-objective optimization problems that consider the evaluation of two fitness functions. This double fitness process, called fg-fitness, evaluates schemata and structures in a common basis. Evolution is conducted considering an adaptive rejection threshold that contemplates both objectives and attributes a rank to each individual in population. The population is dynamic in size and composed of schemata and structures. Recombination preserves good schemata, and mutation is applied to structures to get population diversification. The CGA is applied to two clustering problems in graphs. Representation of schemata and structures use a binary digit alphabet and are based on assignment (greedy) heuristics that provide a clearly distinguished representation for the problems. The clustering problems studied are the classical p-median and the capacitated p-median. Good results are shown for problem instances taken from the literature.
Title: Constructive Genetic Algorithm for Clustering Problems
Description:
Genetic algorithms (GAs) have recently been accepted as powerful approaches to solving optimization problems.
It is also well-accepted that building block construction (schemata formation and conservation) has a positive influence on GA behavior.
Schemata are usually indirectly evaluated through a derived structure.
We introduce a new approach called the Constructive Genetic Algorithm (CGA), which allows for schemata evaluation and the provision of other new features to the GA.
Problems are modeled as bi-objective optimization problems that consider the evaluation of two fitness functions.
This double fitness process, called fg-fitness, evaluates schemata and structures in a common basis.
Evolution is conducted considering an adaptive rejection threshold that contemplates both objectives and attributes a rank to each individual in population.
The population is dynamic in size and composed of schemata and structures.
Recombination preserves good schemata, and mutation is applied to structures to get population diversification.
The CGA is applied to two clustering problems in graphs.
Representation of schemata and structures use a binary digit alphabet and are based on assignment (greedy) heuristics that provide a clearly distinguished representation for the problems.
The clustering problems studied are the classical p-median and the capacitated p-median.
Good results are shown for problem instances taken from the literature.
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