In this paper, we apply an Evolutionary Algorithm (EA) to solve the Rubinstein’s Basic AlternatingOffer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players’ behaviors. Experimental outcomes suggest that the applied co-evolutionary algorithm, one of Evolutionary Algorith...

Low power and reliable thermal design are very important in developing state-of-the-art circuits. This work shows the results obtained with a hybrid of simulating annealing (SA) and a genetic algorithm (GA) and with a hybrid of SA and an evolutionary algorithm (EA) in the generation of inputs pairs that cause the maximum number of switching gates in combinational circuits. We found that both hy...

In this paper, a generational Evolutionary Algorithm (EA) for function optimization is evolved using the Linear Genetic Programming (LGP) technique. Numerical experiments show that the evolved EA significantly outperforms a standard GA.

The major objective of this paper is to deploy an effective evolutionary algorithm (EA) for the congestion problem in connection-oriented networks. The network flow is modeled as non-bifurcated multicommodity flow. The main novelty of this work is that the proposed evolutionary algorithm consists of two levels. The high level applies typical EA operators. The low level idea is based on the hier...

This paper presents an algorithm for solving global optimization problems with bounded variables. The algorithm is a modification of the electromagnetism-like mechanism proposed by Birbil and Fang [J. of Global Optimization 25 (2003), pp. 263-282]. The differences are mainly on the local search procedure and on the force vector used to move each point in the population. Several widely used benc...

‎In this paper, we study a flow shop batch processing machines scheduling problem. The fuzzy due dates are considered to make the problem more close to the reality. The objective function is taken as the weighted sum of fuzzy earliness and fuzzy tardiness. In order to tackle the given problem, we propose a hybrid electromagnetism-like (EM) algorithm, in which the EM is hybridized with a diversi...

A new hybrid PSO-EA-DEPSO algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. This algorithm is shown to outperform RNN predictors trained off-line by PSO, EA, and DEPSO as well as a linear predictor trained by th...

The most simple evolutionary algorithm, the so-called (1+1)EA accepts a child if its fitness is at least as large (in the case of maximization) as the fitness of its parent. The variant (1 + 1)∗EA only accepts a child if its fitness is strictly larger than the fitness of its parent. Here two functions related to the class of long path functions are presented such that the (1 + 1)EA maximizes on...

The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an EA will often become trapped in a suboptimal solution(premature convergence). The ScoutingInspired Evolutionary Algorithm(SEA) is a relatively new technique that avoids premature convergence by determining whether a su...

The selection of appropriate beam irradiation directions in radiotherapy – beam angle optimization (BAO) problem – is very important for the quality of the treatment, both for improving tumor irradiation and for better organs sparing. However, the BAO problem is still not solved satisfactorily and, most of the time, beam directions continue to be manually selected in clinical practice which req...