In order to adapt past experiences to new situations, case-based reasoning algorithms generally rely on domain knowledge and heuristics. Unfortunately, the amount of domain-specific knowledge and domainand task-specific heuristics needed for such case adaptation is usually quite large. In contrast, in our current work we are exploring the use of a knowledge-lean method based on genetic algorith...

This paper presents a comparative analysis of three Reinforcement Learning algorithms (Q-learning, Q(λ)-learning and QSlearning) and their heuristically-accelerated variants (HAQL, HAQ(λ) and HAQS) where heuristics bias action selection, thus speeding up the learning. The experiments were performed in a simulated robot soccer environment which reproduces the conditions of a real competition lea...

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improve...

In this paper we present two hyper-heuristics developed for the Cross-Domain Heuristic Search Challenge. Hyper-heuristics solve hard combinatorial problems by guiding low level heuristics, rather than by manipulating problem solutions directly. Two hyper-heuristics are presented: Five Phase Approach and Genetic Hive. Development paths of the algorithms and testing methods are outlined. Performa...

In recent years, meta-heuristics have become increasingly interesting in solving combinatorial problems including hardware-software partitioning. In this paper, we present a comparative study between some algorithms which involve meta-heuristics based on Tabu search, genetic algorithm and Binary Search Trees to address the problem of hardware-software partitioning. In fact, meta-heuristics can ...

In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-S...

The multisource Weber problem is to locate simultaneously mn facilities in the Euclidean plane to minimize the total transportation cost for satisfying the demand of n fixed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial solut...

An innovative trajectory strategy was proposed and accessed for wire arc additive manufacturing (WAAM), applicable to different more complex geometries, rather than being a single solution. This strategy, named Pixel, can be defined as multitask procedure carry out optimized path planning, whose operation is made through computational algorithms (heuristics), with accessible resources tolerable...