Data Mining Using Adaptive Local Searches Based on Meta-Lamarckian Learning
ABSTRACT
Adaptation of parameters and operators is one of the most important areas of evolutionary computations. In this project we represent local search algorithms for classification of network attacks using the concept of meta-Lamarckian learning. Here, our interests are on the type of evolutionary algorithms which known as adaptive memetic algorithms. One of unique features of adaptive memetic algorithms is the choice of local search methods or memes and recent studies have shown that this choice has significant effects on the performance of problem searches. Methods represented in this project are based on memetic algorithms to improve the rule set got from genetic cycle. These algorithms are incremental and in each stage of algorithm, a new rule is generated and added to rule set. Using represented adaptive local search algorithms, more on the basis of random selection, there are possibilities to optimize rules created in the genetic cycle. These algorithms are used for classification of network attacks on the DARPA data set, which has information on computer networks, and the results and performance are presented in this project.
KEYWORDS
Adaptation algorithm; Memetic algorithm; Evolutionary algorithm; Genetic algorithm; Optimization; Meta-Lamarckian learning; Intrusion detection.