This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (Gonçalves and Resende, 2011) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (1994), differing i...

the jamor purpose of the present research is to predict the total stock market index of tehran stock exchange, using a combined method of wavelet transforms, fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.to do so, first the prediction was made by neural network, then a series of price index was decomposed by w...

An evolutionary metaheuristic called genetic chromodynamics and its applications to optimization, clustering and classification are presented in current paper. Genetic chromodynamics aims at maintaining population diversity and detecting multiple optima. All algorithms derived from genetic chromodynamics use a variable-sized population of solutions and a local interaction principle as selection...

The paper presents a Genetic Algorithm (GA) approach for solving constrained reliability redundancy optimization of general systems. The advanced GA technique uses a dynamic adaptive penalty function to consider the infeasible solutions also and guides the search to optimal or near optimal solution. The penalty technique is applied to keep a certain amount of infeasible solutions in each genera...

This paper addresses a realistic variant of the Hybrid Flow Shop (HFS) problem, based on a real microelectronics manufacturing environment. Firstly, the formulation of a Mixed Integer Linear Programming (MILP) model for optimally solving the problem is provided. Then, two metaheuristic procedures are presented: the former is a genetic algorithm based on an encoding/decoding method well-known in...

This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|fmls, prmu, Splk|∑Cj). The research problem is formulated in form of two different mixed integer linear programming (MILP) models. Comparing with the latest MILP model for the proposed problem in the literature, the complexity size of the propos...

Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fi...

In this paper, a cooperative metaheuristic for the solution of the Unit Commitment problem is presented. This problem is known to be a large scale, mixed integer problem. Due to combinatorial complexity, the exact solution is often intractable. Thus, a metaheuristic based method has to be used to compute a near optimal solution with low computation times. A new approach is presented here. The m...

This article addresses a general tri-objective two-echelon capacitated vehicle routing problem (2E-CVRP) to minimize the total travel cost, customers waiting times and carbon dioxide emissions simultaneously in distributing perishable products. In distributing perishable products, customers’ satisfaction is very important and is inversely proportional to the customers waiting times. The propose...

this study emphasized the ability of genetic algorithm and cellular automate to simulate urban land use changes by integrating adaptive model. the most important part of modeling is to define transition rules.  in this research, a cellular automata model in dinamica ego software was used coupled with genetic algorithm. according to disability of the software for manipulating large number of var...