A hyper-heuristic often represents a heuristic search method that operates over a space of heuristic rules. It can be thought of as a high level searchmethodology to choose lower level heuristics. Nearly 200 papers on hyper-heuristics have recently appeared in the literature. A common theme in this body of literature is an attempt to solve the problems in hand in the following way: at each deci...

HIS work is motivated by the scheduling problem of a real world single machine production encountered in a metal industry. The objective function to consider is the minimization of mean tardiness and flow time. This problem belongs to the class of difficult problems (NP-complete). Due to the dynamic and the difficulty for searching the solution, deterministic searching methods do not work effec...

The aim of the present study is to a new model based on nonlinear singular second order delay differential equation Lane–Emden type and numerically solved by using heuristic technique. Four different examples are presented designed artificial neural networks optimized global search, local search methods their hybrid combinations, respectively, named as genetic algorithm (GA), sequential quadrat...

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning ...

This paper presents an approach to the wordlength allocation and optimization problem for linear digital signal processing systems implemented as custom parallel processing units. Two techniques are proposed, one which guarantees an optimum set of wordlengths for each internal variable, and one which is a heuristic approach. Both techniques allow the user to tradeoff implementation area for ari...

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Acce...

This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyperheuristics has recently been employed to refer to “heuristics that choose heuristics” rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can ope...