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References Publications referenced by this paper. The dendritic cell algorithm Julie Greensmith. Immunology, Sixth Edition. GRASP is a multi-start metaheuristic for combinatorial optimization problems, in which each iteration consists basically of two phases: construction and local search.
The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. An intensification strategy based on path-relinking is frequently used to improve solution quality and to reduce computation times by exploring elite solutions previously found along the search. This chapter describes the basic components of GRASP, successful implementation strategies, and effective hybridizations with path-relinking and other metaheuristics.
We also list some tricks to be used in the quest for good implementations. The bibliography is enriched by an account of relevant applications and by links to surveys, software, and additional sources of material. Variable neighborhood search VNS is a metaheuristic, or framework for building heuristics, which exploits systematically the idea of neighborhood change, both in the descent to local minima and in the escape from the valleys which contain them. In this tutorial we first present the ingredients of VNS, i.
Extensions are presented, in particular Skewed VNS which enhances exploration of far away valleys andvariable neighborhood decomposition search VNDS , a two-level scheme for solution of large instances of various problems.
In each case, we present the scheme, some illustrative examples and questions to be addressed in order to obtain an efficient implementation. One of the central issues in developing neighborhood search techniques is defining the neighborhood.
As a rule of thumb, larger neighborhoods contain higher quality local optimal solutions compared to smaller neighborhoods. However, larger neighborhoods also typically require more time to search than smaller neighborhoods. A neighborhood search algorithm is not practical if the neighborhoods cannot be searched efficiently. Thus, a rapid search algorithm is needed to make efficient use of large neighborhoods.
Constraint satisfaction problems are ubiquitous. A simple example that we will use throughout the first half of this chapter is the following scheduling problem: Choose employees A or B for each of three tasks, X, Y, Z, subject to the work rules that the same employee cannot carry out both tasks X and Y, the same employee cannot carry out both tasks Y and Z, and only employee B is allowed to carry out task Z. Many readers will recognize this as a simple coloring problem. Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives.
The classical means of solving such problems were primarily focused on scalarizing multiple objectives into a single objective, whereas the evolutionary means have been to solve a multi-objective optimization problem as it is.
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In this chapter, we discuss the fundamental principles of multi-objective optimization, the differences between multi-objective optimization and single-objective optimization, and describe a few well-known classical and evolutionary algorithms for multi-objective optimization. Two application case studies reveal the importance of multi-objective optimization in practice.
A number of research challenges are then highlighted. The chapter concludes by suggesting a few tricks of the trade and mentioning some key resources to the field of multi-objective optimization. This tutorial reviews basic concepts in complexity theory, as well as various No Free Lunch results and how these results relate to computational complexity. The tutorial explains basic concepts in an informal fashion that illuminates key concepts.
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Machine learning is a very active sub-field of artificial intelligence concerned with the development of computational models of learning. Machine learning is inspired by the work in several disciplines: cognitive sciences, computer science, statistics, computational complexity, information theory, control theory, philosophy and biology. Simply speaking, machine learning is learning by machine. From a computational point of view, machine learning refers to the ability of a machine to improve its performance based on previous results.
From a biological point of view, machine learning is the study of how to create computers that will learn from experience and modify their activity based on that learning as opposed to traditional computers whose activity will not change unless the programmer explicitly changes it. The derivation of mathematical models that can efficiently describe real-world problems is generally an overwhelming or even impossible task, due to the complexity and inherent ambiguity of characteristics that these problems can possess.
Zadeh , the founder of the theory of fuzzy sets, puts it.
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In this chapter, we are concerned with the discovery of knowledge from data describing a decision situation. A decision situation is characterized by a set of states or examples, which relate the input with the output of the situation. The aim is to find concise knowledge patterns that summarize a decision situation, and that are useful for explanation of this situation, as well as for the prediction of future similar situations.