We have recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics so as to find good quality solutions to given problems. The guided mutation and crossover hyper-GA, the focus of this paper, extends that work. The aim of a guided hyper-GA is to make the dynamic removal and insertion of heuristic...

♣ This work was supported by ITRC under grant number T/500/4704 Abstract: The QoS-based multicast routing is an important issue in the next generation of high-speed multimedia networks. In this paper, we propose a novel QoS-based (multiple constraints) multicast routing algorithm based on the evolutionary algorithms. The connectivity matrix of edges encoding scheme is proposed for genotype repr...

Multicast (MC) routing algorithms capable of satisfying the quality of services(QoS) requirements of real-time applications will be essential for future high-speed networks. Genetic Algorithms (GA) are stochastic search optimisation methods used in combinatorial optimisation and parameter tuning applications. In this paper, a shared-tree routing protocol based on distributed Genetic Algorithms(...

Many practical transport logistics and distribution problems can be formulated as the vehicle routing problem with time windows (VRPTM). The objective is to design an optimal set of routes that services all customers and satisfies the given constraints, especially the time window constraints. The complexity of the VRPTW requires heuristic solution strategies for most real-life instances. Howeve...

Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differenti...

In this paper we compare the eeects of using various stochas-tic operators with the non-unicost set-covering problem. Four diierent crossover operators are compared to a repair heuristic which consists in transforming infeasible strings into feasible ones. These stochastic operators are incorporated in GENEsYs, the genetic algorithm we apply to problem instances of the set-covering problem we d...

The p-center problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients in order to minimize the maximum cost between a client and the facility to which it is assigned. In this article, PBS, a population based meta-heuristic for the p-center problem, is described. PBS is a genetic algorithm based meta-heuristic that uses phenotype crossover and directed...

Genetic Algorithms (GAs) are a search heuristic technique modelled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other sear...

This paper presents a Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) for the solution of Scheduling and Inspection Planning in Software Development Projects. Scheduling and Inspection planning is a vital problem in software engineering whose main objective is to schedule the persons to various activities in the software development process such as coding, inspection, testing an...

Graph partitioning divides a graph into several pieces by cutting edges. The graph partitioning problem is to divide so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges separating these pieces is minimised. Important examples of the problem arise in partitioning graphs known as meshes for the parallel execution of computational mech...