The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high level heuristics during the problem solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism which contains a population of both high quality and diverse solutions that is updated during the problem solving process. The generality of the proposed hyper-heuristic is validated against six well known combinatorial optimization problem, with very different landscapes, provided by the HyFlex software. Empirical results comparing the proposed hyper-heuristic with state of the art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algorithm uses a fixed meme, denoting a hill climbing operator, to improve each solution in a population during the evolutionary search process. Given global parameters and multiple parametrised operators, adaptation often becomes a crucial constituent in the design of MAs. In this study, a self-adaptive self-configuring steady-state multimeme memetic algorithm (SSMMA) variant is proposed. Along with the individuals (solutions), SSMMA co-evolves memes, encoding the utility score for each algorithmic component choice and relevant parameter setting option. An individual uses tournament selection to decide which operator and parameter setting to employ at a given step. The performance of the proposed algorithm is evaluated on six combinatorial optimisation problems from a cross-domain heuristic search benchmark. The results indicate the success of SSMMA when compared to the static Mas as well as widely used self-adaptive Multimeme Memetic Algorithm from the scientific literature

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Improving resiliency using graph based evolutionary algorithms

Resiliency is an important characteristic of any system. It signifies the ability of a system to survive and recover from unprecedented disruptions. Various characteristics exist that indicate the level of resiliency in a system. One of these attributes is the adaptability of the system. This adaptability can be enhanced by redundancy present within the system. In the context of system design, redundancy can be achieved by having a diverse set of good designs for that particular system. Evolutionary algorithms are widely used in creating designs for engineering systems, as they perform well on discontinuous and/or high dimensional problems. One method to control the diversity of solutions within an evolutionary algorithm is the use of combinatorial graphs, or graph based evolutionary algorithms. This diversity of solutions is key factor to enhance the redundancy of a system design. In this work, the way how graph based evolutionary algorithms generate diverse solutions is investigated by examining the influence of representation and mutation. This allows for greater understanding of the exploratory nature of each representation and how they can control the number of solution generated within a trial. The results of this research are then applied to the Travelling [sic] Salesman Problem, a known NP hard problem often used as a surrogate for logistic or network design problems. When the redundancy in system design is improved, adaptability can be achieved by placing an agent to initiate a transfer to other good solutions in the event of a disruption in network connectivity, making it possible to improve the resiliency of the system --Abstract, page iii

Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii

Evolutionary computation applied to combinatorial optimisation problems

This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem. Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search. Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required