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Metaheuristics for the portfolio selection problem
, 2008
"... The Portfolio selection problem is a relevant problem arising in finance and economics. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. Among the most effective approximate algorithms, ..."
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The Portfolio selection problem is a relevant problem arising in finance and economics. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. Among the most effective approximate algorithms, are metaheuristic methods that have been proved to be very successful in many applications. This paper presents an overview of the literature on the application of metaheuristics to the portfolio selection problem, trying to provide a general descriptive scheme.
A Proposition on Memes and MetaMemes in Computing for HigherOrder Learning
"... In computational intelligence, the term ‘memetic algorithm ’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme ’ has been loosely defined as a unit of cultural information, the social analog of genes for in ..."
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In computational intelligence, the term ‘memetic algorithm ’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme ’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm ’ is too specific, and ultimately a misnomer, as much as a ‘meme ’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of highorder memebased learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide muchneeded stimulation to the field of computational intelligence by providing a framework for higher order learning. 1.
Foundations of Stochastic Diffusion Search
, 2004
"... Stochastic Diffusion Search (sds) was introduced by Bishop (1989a) as an algorithm to solve pattern matching problems. It relies on many concurrent partial evaluations of candidate solutions by a population of agents and communication between those agents to locate the optimal match to a target patt ..."
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Stochastic Diffusion Search (sds) was introduced by Bishop (1989a) as an algorithm to solve pattern matching problems. It relies on many concurrent partial evaluations of candidate solutions by a population of agents and communication between those agents to locate the optimal match to a target pattern in a search space. In subsequent research, several variations on the original algorithmic formulation were proposed. It also became evident that its main principles – partial evaluation and communication between agents – can be employed to problems outside the pattern matching domain. The primary aim of this dissertation is to develop these expansive views further: sds is proposed as a metaheuristic, a generic heuristic procedure for solving problems through search. Furthermore, it is proposed as a challenge to the dominant metaphor in computer science: sequential computation. The thesis proceeds in a structured way by first considering all questions that can be asked about a heuristic procedure like sds: questions of a foundational nature, questions pertaining to mathematical analysis, questions about application domains and questions about physical implementation. It is to the foundational issues that most attention is devoted. Analogies with selective processes in natural and social systems are investigated, as well as analogies with other metaheuristic techniques from artificial intelligence. An attempt is made to categorise potential variants, and to establish what kind of problems sds would be the optimal problemsolving method for. The work aims to provide an expanded but structured understanding of sds, to give guidelines for future work, and to establish how progress in other scientific disciplines can be of use in the study of sds, and vice versa. Preface All sciences characterise the essential nature of the systems they study. These characterisations are invariably qualitative in nature, for they set the terms with which more detailed knowledge can be developed. A. Newell and H. Simon (Newell and Simon, 1976) Cybernetics is the science of defensible metaphors.
A distriuted and probabilistic concurrent constraint programming language
, 2005
"... Abstract. We present a version of the CCP paradigm, which is both distributed and probabilistic. We consider networks with a fixed number of nodes, each of them possessing a local and independent constraint store. While locally the computations evolve asynchronously, following the usual rules of (pr ..."
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Abstract. We present a version of the CCP paradigm, which is both distributed and probabilistic. We consider networks with a fixed number of nodes, each of them possessing a local and independent constraint store. While locally the computations evolve asynchronously, following the usual rules of (probabilistic) CCP, the communications among different nodes are synchronous. There are channels, and through them different objects can be exchanged: constraints, agents and channel themselves. In addition, all this activities are embedded in a probabilistic scheme based on a discrete model of time, both locally and globally. Finally we enhance the language with the capability of performing an automatic remote synchronization of variables belonging to different constraint stores. 1
An AgentBased HyperHeuristic Approach to Combinatorial Optimization Problems
"... Abstract. This paper introduces a framework based on multiagent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algorithms simultaneously. By the collaboration of various metaheuristics, we can achieve better results in more classes of ..."
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Abstract. This paper introduces a framework based on multiagent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algorithms simultaneously. By the collaboration of various metaheuristics, we can achieve better results in more classes of problems. Our hyperheuristic approach is defined as a highlevel search in algorithm space implemented within agents. Key words: combinatorial optimization, metaheuristic algorithm, hybrid approach, hyperheuristic, multiagent system 1
Collaboration of Metaheuristic Algorithms through a Multiagent System
"... Abstract. This paper introduces a framework based on multiagent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algorithms simultaneously. By the collaboration of various metaheuristics, we can achieve better results in more classes of p ..."
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Abstract. This paper introduces a framework based on multiagent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algorithms simultaneously. By the collaboration of various metaheuristics, we can achieve better results in more classes of problems. Key words: combinatorial optimization, metaheuristic algorithm, hybrid approach, hyperheuristic, multiagent system 1
Agentbased Protein Structure Prediction
"... A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting the 3D native conformation of a protein, when its sequence of amino acids is known. Although it is accepted that the nativ ..."
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A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting the 3D native conformation of a protein, when its sequence of amino acids is known. Although it is accepted that the native state minimizes the free energy of the protein, all current mathematical models of the problem are affected by intrinsic computational limits, and moreover there is no common agreement on which is the most reliable energy function to be used. In this paper we present an agentbased framework for abinitio simulations, composed by different levels of agents. Each amino acid of an input protein is viewed as an independent agent that communicates with the others. Then we have also strategic agents and cooperative ones. The framework allows a modular representation of the problem and it is easily extensible for further refinements and for different energy functions. Simulations at this level of abstraction allow fast calculation, distributed on each agent. We have written a multithread implementation, and tested the feasibility of the engine with two energy functions.
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"... This paper presents a coalitionbased metaheuristic (CBM) to solve the uncapacitated facility location problem. CBM is a populationbased metaheuristic where individuals encapsulate a single solution and are considered as agents. In comparison to classical evolutionary algorithms, these agents hav ..."
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This paper presents a coalitionbased metaheuristic (CBM) to solve the uncapacitated facility location problem. CBM is a populationbased metaheuristic where individuals encapsulate a single solution and are considered as agents. In comparison to classical evolutionary algorithms, these agents have additional capacities of decision, learning and cooperation. Our approach is also a case study to present how concepts from multiagent systems ’ domain may contribute to the design of new metaheuristics. The tackled problem is a wellknown combinatorial optimization problem, namely the uncapacitated facility location problem, that consists in determining the sites in which some facilities must be set up to satisfy the requirements of a client set at minimum cost. A computational experiment is conducted to test the performance of learning mechanisms and to compare our approach with several existing metaheuristics. The results showed that CBM is competitive with powerful heuristics approaches and presents several advantages in terms of flexibility and modularity.
A coalitionbased metaheuristic for the vehicle routing problem
, 2008
"... Abstract—This paper presents a population based Metaheuristic adopting the metaphor of social autonomous agents. In this context, agents cooperate and selfadapt in order to collectively solve a given optimization problem. From an evolutionary computation point of view, mechanisms driving the searc ..."
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Abstract—This paper presents a population based Metaheuristic adopting the metaphor of social autonomous agents. In this context, agents cooperate and selfadapt in order to collectively solve a given optimization problem. From an evolutionary computation point of view, mechanisms driving the search consist of combining intensification operators and diversification operators, such as local search and mutation or recombination. The multiagent paradigm mainly focuses on the adaptive capabilities of individual agents evolving in a context of decentralized control and asynchronous communication. In the proposed metaheuristic, the agent’s behavior is guided by a decision process for the operators ’ choice which is dynamically adapted during the search using reinforcement learning and mimetism learning between agents. The approach is called CoalitionBased Metaheuristic (CBM) to refer to the strong autonomy conferred to the agents. This approach is applied to the Vehicle Routing Problem to emphasize the performance of learning and cooperation mechanisms. I.
A Multiagent Approach for Metaheuristics Hybridization applied to the Traveling Salesman Problem
"... AbstractThis paper proposes a multiagent approach for metaheuristics hybridization inspired on the popular technique called Particle Swarm Optimization (PSO). In the proposed approach, agents develop a society with collaboration to achieve their own individual as well as common goals and their dec ..."
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AbstractThis paper proposes a multiagent approach for metaheuristics hybridization inspired on the popular technique called Particle Swarm Optimization (PSO). In the proposed approach, agents develop a society with collaboration to achieve their own individual as well as common goals and their decisionmaking process matches the basic nature of a particle in the PSO framework. Each particle is an autonomous agent with memory and methods for learning and making decisions. The proposed approach is applied to the Traveling Salesman Problem in order to test its effectiveness. Hybridization of metaheuristcs; multiagent architecture; traveling salesman problem