Since finding control policies using Reinforcement Learning (RL) can be very time consuming, in recent years several authors have investigated how to speed up RL algorithms by making improved action selections based on heuristics. In this work we present new theoretical results – convergence and a superior limit for value estimation errors – for the class that encompasses all heuristicsbased al...

This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduce...

Recent development of LED technology enabled production of lighting systems with nearly arbitrary light distributions. A nontrivial engineering task is to design a lighting system or a combination of luminaries for a given target light distribution. Here we use heuristics for solving a problem related to this engineering problem, restricted to symmetrical distributions. A genetic algorithm and ...

Randomized search heuristics, among them randomized local search and evolutionary algorithms, are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. The analysis of these randomized search heuristics has been started for some well-known problems, and this approach is followed here for the minimum spanning tree problem. After motivat...

We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory which consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limite...

In the study of heuristics for combinatorial problems, it is often important to develop and compare, in a systematic way, different algorithms, strategies, and parameters for the same problem. This comparison is often biased not only by different implementation languages, but also by different architectures. This paper proposes a framework, described using design patterns, modeling different as...

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational r...

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived...

This thesis aims at exploring the potential of heuristic search algorithms for Abstract Argumentation. Therefore specific backtracking search algorithms are presented, which support the use of heuristics. Thereafter several heuristics are defined which have been implemented as part of this thesis. These will then be experimentally compared among each other and with other approaches to Abstract ...

In this paper, an empirical study on self-generating multimeme memetic algorithms is presented. A set of well known benchmark functions is used during the experiments. Moreover, a heuristic template is introduced for solving timetabling problems. The heuristics designed based on this template can utilize a set of constraint-based hill climbers in a cooperative manner. Two such adaptive heuristi...