Often, in many engineering applications it is required to find the best approximate solution of multi-objective optimization problems quick and with good accuracy. Multi-objective optimization problems are very common and important in real world applications, and as such many researchers are still working to devise various novel heuristic and mathematical approaches for their solution. Mathemat...

Uninformed or blind search, which processes and evaluates all nodes of a search space in the worst case, is not realistic for extracting knowledge from large data sets because of time constraints that are closely related to the dimension of the data. Generally, the search space increases exponentially with problem size, thereby limiting the size of problems that can realistically be solved usin...

A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. Problem solving is the process of finding a solution when the path leading to that solution is uncertain. Even though we are familiar with several problem-solving techniques, in the real world, sometimes many problems cannot be solved b...

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...

Theorem proving essentially amounts to solving search problems. The intricacy of these in general undecidable problems makes the use of appropriate search-guiding heuristics indispensable. However, the appropriateness of a heuristic critically depends on the problem to be solved. Given a set of heuristics to choose from, selecting a suitable heuristic is hence a crucial, but also a very diicult...

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...

Malware is a software which is designed with an intent to damage a network or computer resources. Today, the emergence of malware is on boom letting the researchers develop novel techniques to protect computers and networks. The three major techniques used for malware detection are heuristic, signature-based, and behavior based. Among these, the most prevalent is the heuristic based malware det...

The feature selection problem has been usually addressed through heuristic approaches given its significant computational complexity. In this context, evolutionary techniques have drawn the researchers’ attention owing to their appealing optimization capabilities. In this chapter, promising results achieved by the authors in solving the feature selection problem through a joint effort between r...

We study the one-machine problem with release dates and we look at several objective functions including total (weighted) tardiness and total (weighted) completion time. We describe dominance rules for these criteria, as well as techniques for using these dominance rules to build heuristic solutions. We use them to improve certain well-known greedy heuristic algorithms from the literature. Fina...

In realistic scheduling problems, there may be choices among resources or among process plans. We formulate a constraint-based representation of alternative activities to model problems containing such choices. We extend existing constraint-directed scheduling heuristic commitment techniques and propagators to reason directly about the fact that an activity does not necessarily have to exist in...