In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

the infomax algorithm is a popular method in blind source separation problem. in this article an extension of the infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-gaussian distributions. this ability is the results of using two different nonlinear functions and new coefficients in the learning rule. in this paper we show how we can use the distribution...

in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

This paper proposes an effective multi-objective differential evolution algorithm (MDES) to solve a permutation flow shop scheduling problem (PFSSP) with modified Dejong's learning effect. The proposed algorithm combines the basic differential evolution (DE) with local search and borrows the selection operator from NSGA-II to improve the general performance.  First the problem is encoded with a...

The Infomax algorithm is a popular method in blind source separation problem. In this article an extension of the Infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-Gaussian distributions. This ability is the results of using two different nonlinear functions and new coefficients in the learning rule. In this paper we show how we can use the distribution...

FuzzyBexa was the first algorithm to use a set covering approach for induction of fuzzy classification rules. It followed an iterated concept learning strategy, where rules are induced for each concept in turn. We present a new algorithm to allow also simultaneous concept learning and the induction of ordered fuzzy rule sets. When a proper rule evaluation function is used, simultaneous concept ...