A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem

A revised weight-coded evolutionary algorithm (RWCEA) is proposed for solving multidimensional knapsack problems. This RWCEA uses a new decoding method and incorporates a heuristic method in initialization. Computational results show that the RWCEA performs better than a weight-coded evolutionary algorithm proposed by Raidl (1999) and to some existing benchmarks, it can yield better results than the ones reported in the OR-library.Comment: Submitted to Applied Mathematics and Computation on April 8, 201

An evolutionary algorithm approach to simultaneous multi-mission radar waveform design

It would be beneficial with today’s cluttered electromagnetic spectrum to be able to perform multiple radar missions simultaneously from a single platform. The design of a waveform for this application would greatly benefit the radar community. Radar systems are used to perform many missions, some of which include the detection and tracking of airborne and ground moving targets as well as Synthetic Aperture Radar (SAR) imaging. There are many systems that can operate in multiple modes to perform these missions, although there is no one radar that can simultaneously perform multiple missions using the same waveform [1]. Each mission can be mathematically reduced to an objective or set of objectives that can be used to evaluate their success. These objectives are functions of numerous radar and spatial parameters such as pulse repetition frequency (prf), center frequency, bandwidth, antenna beamwidth, and azimuth look angle, among others. In this thesis, an evolutionary multi-objective optimization technique known as the Strength Pareto Evolutionary Algorithm 2 (SPEA2), developed by Zitzler and Thiele [2], was applied to the simultaneous multi-mission radar waveform design problem. Several of the radar parameters mentioned above were varied to produce diverse waveforms that were manipulated using SPEA2. Due to computational constraints, the problem was approached by using two different scaled down real world scenarios to evaluate the performance of the evolutionary waveform design on a multi-objective moving target indication (MTI) mission and a multi-objective SAR mission, respectively. Multiple experiments showed that SPEA2 can select a set of Pareto optimal waveforms that accomplish these multi-objective missions effectively according to the objective functions that were developed for these missions. Finally, a procedure is outlined to combine these multi-objective MTI and SAR missions into one scaled experiment in which a distributed computing environment could be used to provide more computational resources

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

Copyright © 2012 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences Vol. 183 Issue 1 (2012), DOI: 10.1016/j.ins.2011.07.013We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset

Dynamic Resource Allocation Algorithms for Cognitive Radio Systems

Cognitive Radio (CR) is a novel concept for improving the utilization of the radio spectrum. This promises the efficient use of scarce radio resources. Orthogonal Frequency Division Multiplexing (OFDM) is a reliable transmission scheme for Cognitive Radio Systems which provides flexibility in allocating the radio resources in dynamic environment. It also assures no mutual interference among the CR radio channels which are just adjacent to each other. Allocation of radio resources dynamically is a major challenge in cognitive radio systems. In this project, various algorithms for resource allocation in OFDM based CR systems have been studied. The algorithms attempt to maximize the total throughput of the CR system (secondary users) subject to the total power constraint of the CR system and tolerable interference from and to the licensed band (primary users). We have implemented two algorithms Particle Swarm Algorithm(PSO) and Genetic Algorithm(GA) and compared their results

Two-dimensional placement compaction using an evolutionary approach: a study

The placement problem of two-dimensional objects over planar surfaces optimizing given utility functions is a combinatorial optimization problem. Our main drive is that of surveying genetic algorithms and hybrid metaheuristics in terms of final positioning area compaction of the solution. Furthermore, a new hybrid evolutionary approach, combining a genetic algorithm merged with a non-linear compaction method is introduced and compared with referenced literature heuristics using both randomly generated instances and benchmark problems. A wide variety of experiments is made, and the respective results and discussions are presented. Finally, conclusions are drawn, and future research is defined

Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area