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26
A Tutorial for Competent Memetic Algorithms: Model, Taxonomy And Design Issues
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
"... The combination of Evolutionary algorithms with local search was named "Memetic Algorithms" (MAs) in [1]. 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 a ..."
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Cited by 69 (8 self)
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The combination of Evolutionary algorithms with local search was named "Memetic Algorithms" (MAs) in [1]. 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 metaheuristics 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.
A Weighted Coding in a Genetic Algorithm for the DegreeConstrained Minimum Spanning Tree Problem
, 2000
"... is a fundamental design choice in a genetic algorithm. This paper describes a novel coding of spanning trees in a genetic algorithm for the degreeconstrained minimum spanning tree problem. For a connected, weighted graph, this problem seeks to identify the shortest spanning tree whose degree does n ..."
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Cited by 18 (4 self)
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is a fundamental design choice in a genetic algorithm. This paper describes a novel coding of spanning trees in a genetic algorithm for the degreeconstrained minimum spanning tree problem. For a connected, weighted graph, this problem seeks to identify the shortest spanning tree whose degree does not exceed an upper bound k 2. In the coding, chromosomes are strings of numerical weights associated with the target graph's vertices. The weights temporarily bias the graph's edge costs, and an extension of Prim's algorithm, applied to the biased costs, identifies the feasible spanning tree a chromosome represents. This decoding algorithm enforces the degree constraint, so that all chromosomes represent valid solutions and there is no need to discard, repair, or penalize invalid chromosomes. On a set of hard graphs whose unconstrained minimum spanning trees are of high degree, a genetic algorithm that uses this coding identifies degreeconstrained minimum spanning trees that are on average shorter than those found by several competing algorithms.
Topological Design of Communication Networks using Multiobjective Genetic Optimization
"... Designing communication networks is a complex, multiconstraint and multicriterion optimization problem. We present a multiobjective genetic optimization approach to setting up a network while simultaneously minimizing network delay and installation cost subject to reliability and flow constraints. ..."
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Cited by 9 (1 self)
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Designing communication networks is a complex, multiconstraint and multicriterion optimization problem. We present a multiobjective genetic optimization approach to setting up a network while simultaneously minimizing network delay and installation cost subject to reliability and flow constraints. In this work we use a Pareto Converging Genetic Algorithm, present results for two test networks, and compare results with another heuristic method.
Adaptive Reconfiguration of Data Networks Using Genetic Algorithms
, 2002
"... Genetic algorithms are applied to an important, but littleinvestigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traf ..."
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Cited by 8 (0 self)
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Genetic algorithms are applied to an important, but littleinvestigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traffic patterns; and the quality of service (QoS) requirements and priorities of different users and applications. Dynamic reconfiguration is possible in networks that contain links whose endpoints can be easily changed, such as satellite channels or terrestrial wireless connections. We report results that demonstrate the feasibility of performing genetic search quickly enough for online adaptation.
A Webbased Evolutionary Model for Internet Data Caching
, 1999
"... Caching is a standard solution to the problem of insufficient bandwidth caused by the rapid increase of information circulation across the Internet. Cache consistency mechanisms areacrucialcomponent of each cache scheme influencing the cache usefulness and reliability. This paper presents a model fo ..."
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Cited by 7 (5 self)
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Caching is a standard solution to the problem of insufficient bandwidth caused by the rapid increase of information circulation across the Internet. Cache consistency mechanisms areacrucialcomponent of each cache scheme influencing the cache usefulness and reliability. This paper presents a model for optimizing Internet cache content by the use of a genetic algorithm and examines the model by tracedriven experiments. Cached data are considered as a population evolving over simulated time by a number of successive cache "generations". The model is testedbythe use of traces providedbyaSquidproxy cache server. Using tracedriven caching, we show that the proposed evolutionary mechanisms improve cache nonstaleness and consistency and result in an updated cache content.
Multicriteria network design using evolutionary algorithm
 Proc. Genetic and Evolutionary Computations Conference (GECCO), Lecture Notes in Computer Sciences
, 2003
"... Abstract. In this paper, we revisit a general class of multicriteria multiconstrained network design problems and attempt to solve, in a novel way, with Evolutionary Algorithms (EAs). A major challenge to solving such problems is to capture possibly all the (representative) equivalent and diverse ..."
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Cited by 6 (3 self)
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Abstract. In this paper, we revisit a general class of multicriteria multiconstrained network design problems and attempt to solve, in a novel way, with Evolutionary Algorithms (EAs). A major challenge to solving such problems is to capture possibly all the (representative) equivalent and diverse solutions. In this work, we formulate, without loss of generality, a bicriteria bi constrained communication network topological design problem. Two of the primary objectives to be optimized are network delay and cost subject to satisfaction of reliability and flowconstraints. This is a NPhard problem so we use a hybrid approach (for initialization of the population) along with EA. Furthermore, the twoobjective optimal solution front is not known a priori. Therefore, we use a multiobjective EA which produces diverse solution space and monitors convergence; the EA has been demonstrated to work effectively across complex problems of unknown nature. We tested this approach for designing networks of different sizes and found that the approach scales well with larger networks. Results thus obtained are compared with those obtained by two traditional approaches namely, the exhaustive search and branch exchange heuristics. 1
Estimating AllTerminal Network Reliability Using a Neural Network
"... The exact calculation of allterminal network reliability is an NPhard problem, with computational effort growing exponentially with the number of nodes and links in the network. Because of the impracticality of calculating allterminal network reliability for networks of moderate to large size, Mon ..."
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Cited by 5 (2 self)
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The exact calculation of allterminal network reliability is an NPhard problem, with computational effort growing exponentially with the number of nodes and links in the network. Because of the impracticality of calculating allterminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of allterminal network reliability that of artificial neural network predictive models. Neural networks are constructed, trained and validated using alternative network topologies, a network reliability upper bound and the exact network reliability as a target. A hierarchical approach is used: a general neural network screens all network designs for reliability followed by a specialized neural network for highly reliable network designs. Results on a ten node problem are given using a grouped cross validation approach.
Designing Reliable Communication Networks with a Genetic Algorithm Using a Repair Heuristic
, 2003
"... This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are poss ..."
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Cited by 5 (1 self)
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This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are possible. We show that existing approaches using penalty functions can result in invalid solutions and are therefore not appropriate for solving this problem. To overcome these problems we present a repair heuristic, which is based on the number of spanning trees in a graph. This heuristic always generates a valid solution, which when compared to a greedy cheapest repair heuristic shows that the new approach finds better solutions with less computational effort.
An Improved General Upper Bound for AllTerminal Network Reliability
 PROCEEDINGS OF THE INDUSTRIAL ENGINEERING RESEARCH CONFERENCE
, 1998
"... The exact calculation of allterminal network reliability is an NPhard problem, precluding its use in the design of optimal network topologies for problems of realistic size. This paper presents an improved upper bound for allterminal network reliability that is significantly more general than p ..."
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Cited by 4 (2 self)
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The exact calculation of allterminal network reliability is an NPhard problem, precluding its use in the design of optimal network topologies for problems of realistic size. This paper presents an improved upper bound for allterminal network reliability that is significantly more general than previously published bounds as it allows arcs of different reliabilities. The bound is calculated in polynomial time, making it computationally practical for large networks. The bound's effectiveness is demonstrated on 108 network problems for both identical arc reliabilities and differing arc reliabilities. It is shown that the new bound yields statistically better results than a previously published bound with no increase in computational effort, and more importantly, the new bound can be used for network design problems that may include arcs with different reliabilities.