Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

A survey of induction algorithms for machine learning

Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented

Computational experience with a branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations.

In a previous paper (De Reyck and Herroelen, 1996a), we presented an optimal procedure for the resource-constrained project scheduling problem (RCPSP) with generalised precedence relations (further denoted as RCPSP-GPR) with the objective of minimizing the project makespan. The RCPSP-GPR extends the RCPSP to arbitrary minimal and maximal time lags between the starting and completion times of activities. The procedure is a depth-first branch -and-bound algorithm in which the nodes in the search tree represent the original project network extended with extra precedence relations, which resolve a resource conflict present in the project network of the parent node. Resource conflicts are resolved using the concept of minimal delaying alternatives, i.e. minimal sets of activities which, when delayed, release enough resources to resolve the conflict. Precedence- and resource-based lower bounds as well as dominance rules are used to fathom large portions of the search tree. In this paper we report new computational experience with the algorithm using a new RCPSP-GPR random problem generator developed by Schwindt (1995). A comparison with other computational results reported in the literature is included.Scheduling;

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Planning Graph Heuristics for Belief Space Search

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning