We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics compo...

This paper analyzes the use of expert systems in production management and provides both an historical perspective and a review of some recently developed expert systems for production management problems. Current expert systems in production management fill two gaps associated with traditional optimization and heuristic methods. First, optimization and heuristic methods do not process semantic...

Scheduling in a permutation flowshop refers to processing of jobs set available machines the same order. Among several possible performance characteristics flowshop, makespan remains one highest preferred metrics by researchers past six decades. The constructive heuristic proposed Nawaz-Enscore-Ham (NEH) is best for minimization. essentially depends on initial ordering according particular prio...

In many planning domains, it is possible to define and learn good rules for reactively selecting actions. This has lead to work on learning rule-based policies as a form of planning control knowledge. However, it is often the case that such learned policies are imperfect, leading to planning failure when they are used for greedy action selection. In this work, we seek to develop a more robust f...

in this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. two objective functions, real power loss and energy not supplied index (ens), were utilized. multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...

We present an architecture for rule induction that emphasizes compact, reduced-complexity rules. A new heuristic technique for finding a covering rule set of sample data is described. This technique refines a set of production rules by iteratively replacing a component of a rule with its single best replacement. A method for rule induction has been developed that combines this covering and refi...

In rule learning, rules are typically induced in two phases, rule refinement and rule selection. It was recently argued that the usage of two separate heuristics for each phase—in particular using the so-called inverted heuristic in the refinement phase—produces longer rules with comparable classification accuracy. In this paper we test the utility of inverted heuristics in the context of subgr...

Orlandi and Siebrand [1] have shown that radiationless transitions in molecules are subject to an electronic propen­ sity rule which does not refer directly to the states involved in the transition but instead to the radiative transition with which it competes. In a series of related molecules higher radiative rate constants are accompanied by higher rate con­ stants for the competing radiation...

Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...

Meta-heuristics are high-level approaches developed to discover a heuristic that provides reasonable solution many varieties of optimization problems. The classification problems contain sort problem. Simply, the objective herein is reduce number misclassified instances. In this paper, question whether meta-heuristic methods can be used construct linear models or not answered. To end, Particle ...