in this study, we present a mrcpsp/max (multi-mode resource-constrained project scheduling problem with minimum and maximum time lags) model with minimization tardiness costs and maximization earliness rewards of activities as objective. the proposed model is nearby to real-world problems and has wide applications in various projects. this problem is not available in the literature exactly and ...

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC has slow convergence rate when it used solve complex optimization problems since solution search equation more of an exploration than exploitation operator. This paper presents improved for solving integer programming minimax problems. pro...

In this paper we use marker method and propose a new mutation operator that selects the nearest neighbor among all near neighbors solving Traveling Salesman Problem.

There is increasing interest in evolutionary algorithms that have variable-length genomes and/or location independent genes. However, our understanding of such algorithms both theoretically and empirically is much less well developed than the more traditional xed-length, xed-location ones. Recent studies with VIV (VIrtual Virus), a variable length, GA-based computational model of viral evolutio...

In the field of artificial intelligence, one of the hardest things that we can try to make a computer program do is to interact with the real world. In contrast to the well-defined, discrete, simplified world that programs normally operate in, the real world is large, unknown, and complex. In the real world, programs must learn and adapt to new and changing situations in order to be effective. ...

While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providi...

This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called particle swarm optimization with mutation operators (PSOM). The mutation operators are ...

we introduce a new concept of general $g$-$eta$-monotone operator generalizing the general $(h,eta)$-monotone operator cite{arvar2, arvar1}, general $h-$ monotone operator cite{xiahuang} in banach spaces, and also generalizing $g$-$eta$-monotone operator cite{zhang}, $(a, eta)$-monotone operator cite{verma2}, $a$-monotone operator cite{verma0}, $(h, eta)$-monotone operator cite{fanghuang}, $h$-...

The aim of this work is to analyze the influence of the representation’s modification on the behaviour of an adaptive representation evolutionary algorithm based only on a mutation operator. A statistical analysis of the mutation properties suggests some strategies for the modification of the representation’s base. The influence of these strategies is numerically analyzed on some classical test...

In this paper, a concept of directional mutations for phenotypic evolutionary algorithms is presented. The proposed approach allows, in a very convenient way, to adapt the probability measure underlying the mutation operator during evolutionary process. Moreover -stable distributions are used to generate the mutation radius. A correlation analysis between control parameters – stability index , ...