The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...

Motivated by the natural immune system’s ability to defend the body by generating and maintaining a repertoire of antibodies that collectively cover the potential pathogen space, we describe an artificial system that discovers and maintains a repertoire of heuristics that collectively provide methods for solving problems within a problem space. Using bin-packing as an example domain, the system...

Hyper-heuristics can be thought of as heuristic management methodologies which are motivated by the goal of building a high level of generality in the scope of computational search methodologies [2] [7]. Most meta-heuristics (and other search methods) perform a search over the solution space directly, whereas, hyper-heuristics as high level strategies search a heuristic space. One of the hyper-...

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...

Vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. The problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper propose...

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated Local Search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by...

The minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for ...

Abstract This paper investigates the performance of several meta-heuristic algorithms, including Particle Swarm Optimisation (PSO), different variants Differential Evolution (DE), Biogeography-Based (BBO), Cultural Algorithm (CA), Optics-Inspired (OIO), and League Championship (LCA), for optimum layup laminated composite plates. The study provides detailed Pseudo codes algorithms. buckling capa...