This paper proposes an evolutionary-based iterative local search hyper-heuristic approach called Iterated Search Driven by Evolutionary Algorithm Hyper-Heuristic (ISEA). Two versions of this algorithm, ISEAchesc and ISEA-adaptive, that differ in the re-initialization scheme are presented. The performance of the two algorithms was experimentally evaluated on six hard optimization problems using ...

This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the...

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selectio...

The main principle of a look-ahead strategy is to inspect a few steps ahead before taking a decision on the direction to choose. We propose two original look-ahead strategies that differ in the object of inspection. The first method introduces a look-ahead mechanism at a superior level for selecting local-search operators. The second method uses a look-ahead strategy on a lower level in order t...

A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas; on the other hand, clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arb...

The problem of assigning gates to arriving and departing flights is one of the most important problems in airport operations. We take into account the real multi-criteria nature of the problem by optimizing a total of nine gate allocation objectives that are oriented both on convenience for airport/airline services and passenger comfort. As far as we are aware, this is the largest number of obj...

We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the minibucket heuristic. We present and analyze methods...

The planning of a canning line at a drinks manufacturer is discussed and formulated as a mathematical programming model. Several alternative heuristic solution methods are developed, tested and compared on real data, illustrating the trade-offs between solution quality and computing time. The two most successful methods make hybrid use of local search and integer programming, but in rather diff...

Exact optimization algorithms are not able to provide an appropriate solution in solving optimization problems with a high-dimensional search space. In these problems, the search space grows exponentially with the problem size therefore; exhaustive search is not practical. Also, classical approximate optimization methods like greedy-based algorithms make several assumptions to solve the problem...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...