Genetic Algorithms (GAs) are a class of optimization algorithms inspired by the process of natural selection. They are used to find approximate solutions to optimization and search problems through bio-inspired operations such as selection, crossover, and mutation.
GAs start with a population of potential solutions, represented as chromosomes. Each chromosome is evaluated using a fitness function, which measures how well it solves the problem at hand. The fittest chromosomes are selected to create a new generation through crossover and mutation. This process is repeated for several generations, with the goal of evolving better solutions over time.
Genetic Algorithms have been successfully applied to a wide range of problems, including:
High Utility Itemset mining is a task in data mining that aims to find itemsets in transactional databases with high utility. The utility of an itemset is a measure that can reflect various aspects such as profit, cost, or other user-defined criteria.
GAs can be effectively used to find high utility itemsets in large databases. The process involves encoding itemsets as chromosomes and defining a fitness function that reflects the utility of each itemset. Through the iterative process of selection, crossover, and mutation, the GA searches for itemsets that maximize the utility.
The main steps involved are:
Author: Võ Hoàng Thái Đạt