We present evidence indicating that adding a crossover island greatly improves the performance of a Dynamic Island Model for Adaptive Operator Selection. Two combinatorial optimisation problems are considered: the Onemax benchmark, to prove the concept; and a real-world formulation of the course timetabling problem to test practical relevance. Crossover is added to the recently proposed dynamic...

a compact formulation of the set of tours neither in a graph nor its complement is presented and illustrates a general methodology proposed for constructing polyhedral models of decision problems based upon permutations, projection and lifting techniques. directed hamilton tours on n vertex graphs are interpreted as (n-1)- permutations. sets of extrema of birkhoff polyhedra are mapped to tours ...

The reconstruction of founder genetic sequences of a population is a relevant issue in evolutionary biology research. The problem consists in finding a biologically plausible set of genetic sequences (founders), which can be recombined to obtain the genetic sequences of the individuals of a given population. The reconstruction of these sequences can be modelled as a combinatorial optimisation p...

A safe control of evolution consists in preventing past errors of evolution to be repeated, which could be done by keeping track of the history of evolution. But maintaining and exploiting the complete history is intractable. This paper therefore investigates the use of machine learning (ML), in order to extract a manageable information from this history. More precisely, induction from examples...

The Vehicle Routing Problem (VRP) is a NP-hard and Combinatorial optimization problem. Combinatorial optimization problem can be viewed as searching for best element in a set of discrete items, which can be solved using search algorithm or meta heuristic. In this work, VRP is solved using population based search algorithm, Particle Swarm Optimization (PSO) with crossover and mutation operators....

This paper proposes a new crossover method for parallel distributed genetic algorithms (PDGAs). PDGAs with multiple subpopulations provide better solutions than conventional GAs with a single population. The proposed method, including the hybridization crossover and the best combinatorial crossover, is designed to increase the performance of PDGAs. The proposed method, which provides high local...

Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the Multidimensional Knapsack problem. Common mutation operators (like bit-flip mutation) and classic 1-point and uniform crossover are employed to generate fit...

A very promising approach for combinatorial optimization is evolutionary algorithms. As an application, this paper deals with the strict strong graph coloring problem defined by Haddad and Kheddouci (2009) where the authors have proposed an exact polynomial time algorithm for trees. The aim of this paper is to introduce a new evolutionary algorithm for solving this problem for general graphs. I...

The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionar...