Differential Evolution (DE) is one of the most popular optimization methods for real-valued
problems and a large number of its variants have been proposed so far. However, bringing …

Due to abundance of novel optimization algorithms in recent years, the problem of large
similarities among methods that are named differently is becoming troublesome and …

Many real-world engineering problems such as machining processes are multi-objective
optimization problems (MOPs) because multiple performance characteristics are considered …

Feedforward neural network (FNN) emerges as a promising machine learning algorithm
widely used for crucial tasks such as prediction, classification, clustering, regression, and …

Most of the well-established particle swarm optimization (PSO) variants do not provide
alternative learning strategies when particles fail to improve their fitness during the …

The deviation between the modelling of teaching-learning based optimization (TLBO)
framework and actual scenario of classroom teaching and learning process is considered as …

This study examines the possibility that the computational intelligence (CI) inspired tools can
effectively aggregate the rich information generated from the Web 2.0 economy and …

Peirce's theory of evolution gives a highly efficient optimiza-tion algorithm in the domain of
evolutionary computation (EC). Peircean Evolutionary Algorithm (P-EA) has the potential to …

Abstract Development of decision-support and intelligent agent systems necessitates
mathematical descriptions of uncertainty and fuzziness in order to model vagueness. This …

Związki między biologią a informatyką sięgają samych początków historii współczesnego
komputera1, czyli mniej więcej lat pięćdzie! siątych ubiegłego wieku. Współpraca ta jest …