Jim Smith - Profile on Academia.edu (original) (raw)

Papers by Jim Smith

Teaching problem solving and AI with PacMan

Lecture Notes in Computer Science, 2005

AIS, immune systems, gene libraries, meta learning, Baldwin effect Artificial Immune Systems (AIS... more AIS, immune systems, gene libraries, meta learning, Baldwin effect Artificial Immune Systems (AIS) have been shown to be useful, practical and realisable approaches to real-world problems. Most AIS implementations are based around a canonical algorithm such as clonotypic learning, which we may think of as individual, lifetime learning. Yet a species also learns. Gene libraries are often thought of as a biological mechanism for generating combinatorial diversity of antibodies. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding the lifetime learning mechanisms. Over time, the gene libraries in a species will evolve to an appropriate bias for the expected environment (based on species memory). Thus, gene libraries are a form of meta-learning which could be useful for AIS. Yet they are hardly ever used. In this paper we consider some of the possible benefits and implications of incorporating the evolution of gene libraries into AIS practice. We examine some of the issues that must be considered if the implementation is to be successful and beneficial.

A memetic algorithm with self-adaptive local search: TSP as a case study

In this paper we introduce a promising hy-bridization scheme for a Memetic Algorithm (MA). Our MA... more In this paper we introduce a promising hy-bridization scheme for a Memetic Algorithm (MA). Our MA is composed of two optimiza-tion processes, a Genetic Algorithm and a Monte Carlo method (MC). In contrast with other GA-Monte Carlo hybridized memetic algorithms, in ...

IEEE Transactions on Evolutionary Computation, 2005

The combination of Evolutionary algorithms with local search was named "Memetic Algorithms" (MAs)... more The combination of Evolutionary algorithms with local search was named "Memetic Algorithms" (MAs) in . These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement [2]. In the case of MAs "memes" refer to the strategies (e.g. local refinement, perturbation or constructive methods, etc) that are employed to improve individuals. In this paper we review some works on the application of MAs to well known combinatorial optimisation problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of meta-heuristics it is possible to explore their design space and better understand their behaviour from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient Memetic Algorithms.

This paper reports a lot-sizing and scheduling problem, which minimizes inventory and backlog cos... more This paper reports a lot-sizing and scheduling problem, which minimizes inventory and backlog costs of multiple products on M parallel machines with sequence-dependent setup times over T periods. Problem solutions are represented as product subsets (ordered or unordered) for each machine m at each period t. The optimal lot sizes are then determined applying a linear program. A genetic algorithm searches either over ordered or over unordered subsets (which are implicitly ordered using a fast ATSPtype heuristic) to try to identify an optimal solution. Initial computational results are presented, comparing the speed and solution quality of the ordered and unordered genetic algorithm approaches.

Recent Advances in Memetic Algorithms and Related Search Technologies

Memetic Evolutionary Algorithms

Studies in Fuzziness and Soft Computing, 2005

Memetic Evolutionary Algorithms (MAS) are a class of stochastic heuristics for global optimizatio... more Memetic Evolutionary Algorithms (MAS) are a class of stochastic heuristics for global optimization which combine the parallel global search nature of Evolutionary Algorithms with Local Search to improve individual solutions. These techniques are being applied to an increasing ...

An Examination of Tuneable, Random Search Landscapes

What are Evolutionary Algorithms?

Feature Selection for Heterogeneous Ensembles of Nearest-neighbour Classifiers Using Hybrid Tabu Search

Natural Computing Series, 2008

Working with Evolutionary Algorithms

Natural Computing Series, 2003

Scaling up a hybrid genetic linear programming algorithm for statistical disclosure control

Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, 2011

Abstract This paper looks at the real world problem of statistical disclosure control. National S... more Abstract This paper looks at the real world problem of statistical disclosure control. National Statistics Agencies are required to publish detailed statistics and simultaneously guarantee the confidentiality of the contributors. When published statistical tables contain magnitude data such as turnover or health statistics the preferred method is to suppress the values of cells which may reveal confidential information. However suppressing these&amp;amp;#x27;primary&amp;amp;#x27;cells alone will not guarantee protection due the presence of margin (row/column) totals and ...

Parallel Problem Solving from Nature – PPSN XIII

Lecture Notes in Computer Science, 2014

Parallel Problem Solving from Nature - PPSN VIII

Lecture Notes in Computer Science, 2004

... of the Basque Country, Computer Science Faculty P. Manuel de Lardizabal, 1, 20009 ... Jesus S... more ... of the Basque Country, Computer Science Faculty P. Manuel de Lardizabal, 1, 20009 ... Jesus S. Aickelin, Uwe Alander, Jarmo Alba, Enrique Altenberg, Lee Araujo, Lourdes Baeck, Thomas ... Hoc Networks 461 Gianni Di Caro, Frederick Ducatelle, and Luca Maria Gambardella A ...

Memetic Algorithms: The Polynomial Local Search Complexity Theory Perspective

Journal of Mathematical Modelling and Algorithms, 2008

Abstract In previous work (Krasnogor, http://www. cs. nott. ac. uk/~ nxk/papers. html. In: Studie... more Abstract In previous work (Krasnogor, http://www. cs. nott. ac. uk/~ nxk/papers. html. In: Studies on the Theory and Design Space of Memetic Algorithms. Ph. D. thesis, University of the West of England, Bristol, UK, 2002; Krasnogor and Smith, IEEE Trans Evol Algorithms ...

IEEE Transactions on Evolutionary Computation, 1999

The issue of controlling values of various parameters of an evolutionary algorithm is one of the ... more The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: It has a potential of adjusting the algorithm to the problem while solving the problem. In this paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

What is an evolutionary algorithm?

Self adaptation in evolutionary algorithms

Abstract Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of Natura... more Abstract Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of Natural Selection. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the ...

MAFRA: A java memetic algorithms framework

In this paper we will introduce the Memetic Algorithms FRAmework, a general pur-pose evolutionary... more In this paper we will introduce the Memetic Algorithms FRAmework, a general pur-pose evolutionary computation framework. MAFRA allows the construction of complex evolutionary systems with a maximum of reuse between different instantiations of the framework. MAFRA has been ...