Antonio J. Fernández

On the Hybridization of Memetic Algorithms With Branch-and-Bound Techniques

Branch-and-bound (BnB) and memetic algorithms represent two very different approaches for tackling combinatorial optimization problems. However, these approaches are compatible. In this correspondence, a hybrid model that combines these two techniques is considered. To be precise, it is based on the interleaved execution of both approaches. Since the requirements of time and memory in BnB techniques are generally conflicting, a truncated exact search, namely, beam search, has opted to be carried out. Therefore, the resulting hybrid algorithm has a heuristic nature. The multidimensional 0-1 knapsack problem and the shortest common supersequence problem have been chosen as benchmarks. As will be shown, the hybrid algorithm can produce better results in both problems at the same computational cost, especially for large problem instances

A Comparative Study of Eight Constraint Programming LanguagesOver the Boolean and Finite Domains

This paper compares the efficiency of a number of Constraint Logic Programming (CLP) systems in the setting of finite domains as well as a specific aspect of their expressiveness (that concerning reification and meta-constraints). There are two key reasons for adopting CLP technology for solving a problem. The first is its expressiveness enabling a declarative solution with readable code which is vital for maintenance and the second is the provision of an efficient implementation for the computationally expensive procedures. However, CLP systems differ significantly both in how solutions may be expressed and the efficiency of their execution and it is important that both these factors are taken into account when choosing the best CLP system for a particular application. This paper aids this choice by illustrating differences between the systems, indicating their particular strengths and weaknesses.

Eventual Leader Election with Weak Assumptions on Initial Knowledge, Communication Reliability, and Synchrony

This paper considers the eventual leader election problem in asynchronous message-passing systems where an arbitrary number t of processes can crash (t<n, where n is the total number of processes). It considers weak assumptions both on the initial knowledge of the processes and on the network behavior. More precisely, initially, a process knows only its identity and the fact that the process identities are different and totally ordered (it knows neither n nor t). Two eventual leader election protocols are presented. The first protocol assumes that a process also knows the lower bound alpha on the number of processes that do not crash. This protocol requires the following behavioral properties from the underlying network: the graph made up of the correct processes and fair lossy links is strongly connected, and there is a correct process connected to t-f other correct processes (where f is the actual number of crashes in the considered run) through eventually timely paths (paths made up of correct processes and eventually timely links). This protocol is not communication-efficient in the sense that each correct process has to send messages forever. The second protocol is communication-efficient: after some time, only the final common leader has to send messages forever. This protocol does not require the processes to know alpha, but requires stronger properties from the underlying network: each pair of correct processes has to be connected by fair lossy links (one in each direction), and there is a correct process whose output links to the rest of correct processes have to be eventually timely. This protocol enjoys also the property that each message is made up of several fields, each of which taking values from a finite domain

Finding low autocorrelation binary sequences with memetic algorithms

This paper deals with the construction of binary sequences with low autocorrelation, a very hard problem with many practical applications. The paper analyzes several metaheuristic approaches to tackle this kind of sequences. More specifically, the paper provides an analysis of different local search strategies, used as stand-alone techniques and embedded within memetic algorithms. One of our proposals, namely a memetic algorithm endowed with a Tabu Search local searcher, performs at the state-of-the-art, as it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature. Moreover, this algorithm is also able to provide new best-known solutions for large instances of the problem. In addition, a variant of this algorithm that explores only a promising subset of the whole search space (known as skew-symmetric sequences) is also analyzed. Experimental results show that this new algorithm provides new best-known solutions for very large instances of the problem.

Constraint functional logic programming over finite domains

In this paper, we present our proposal to Constraint Functional Logic Programming over Finite Domains (CFLP(ℱ𝐷) with a lazy functional logic programming language which seamlessly embodies finite domain (ℱ𝐷) constraints. This proposal increases the expressiveness and power of constraint logic programming over finite domains (CLP(ℱ𝐷) by combining functional and relational notation, curried expressions, higher-order functions, patterns, partial applications, non-determinism, lazy evaluation, logical variables, types, domain variables, constraint composition, and finite domain constraints. We describe the syntax of the language, its type discipline, and its declarative and operational semantics. We also describe 𝑇𝑂𝑌 (ℱ𝐷), an implementation for CFLP(ℱ𝐷), and a comparison of our approach with respect to CLP(ℱ𝐷) from a programming point of view, showing the new features we introduce. And, finally, we show a performance analysis which demonstrates that our implementation is competitive with respect to existing CLP(ℱ𝐷) systems and that clearly outperforms the closer approach to CFLP(ℱ𝐷).

Solving Combinatorial Problems with a Constraint Functional Logic Language

This paper describes a proposal to incorporate finite domain constraints in a functional logic system. The proposal integrates functions, higher-order patterns, partial applications, non-determinism, logical variables, currying, types, lazyness, domain variables, constraints and finite domain propagators.The paper also presents TOY(FD), an extension of the functional logic language TOYthat provides FD constraints, and shows, by examples, that TOY(FD) combines the power ofconstraint logic programming with the higher-order characteristics of functional logic programming.

Local Search-based Hybrid Algorithms for Finding Golomb Rulers

The Golomb ruler problem is a very hard combinatorial optimization problem that has been tackled with many different approaches, such as constraint programming (CP), local search (LS), and evolutionary algorithms (EAs), among other techniques. This paper describes several local search-based hybrid algorithms to find optimal or near-optimal Golomb rulers. These algorithms are based on both stochastic methods and systematic techniques. More specifically, the algorithms combine ideas from greedy randomized adaptive search procedures (GRASP), scatter search (SS), tabu search (TS), clustering techniques, and constraint programming (CP). Each new algorithm is, in essence, born from the conclusions extracted after the observation of the previous one. With these algorithms we are capable of solving large rulers with a reasonable efficiency. In particular, we can now find optimal Golomb rulers for up to 16 marks. In addition, the paper also provides an empirical study of the fitness landscape of the problem with the aim of shedding some light about the question of what makes the Golomb ruler problem hard for certain classes of algorithm.

A Hybrid GRASP - Evolutionary Algorithm Approach to Golomb Ruler Search

We consider the problem of finding small Golomb rulers, a hard combinatorial optimization task. This problem is here tackled by means of a hybrid evolutionary algorithm (EA). This EA incorporates ideas from greedy randomized adaptive search procedures (GRASP) in order to perform the genotype-to-phenotype mapping. As it will be shown, this hybrid approach can provide high quality results, better than those of reactive GRASP and other EAs.

Memetic Algorithms in Planning, Scheduling, and Timetabling

Memetic algorithms (MAs) constitute a metaheuristic op- timization paradigm based on the systematic exploitation of knowledge about the problem being solved, and the synergistic combination of ideas taken from other population-based and trajectory-based metaheuristics. They have been successfully deployed on a plethora of hard combina- torial optimization problems, amongst which scheduling, planning and timetabling are distinguished examples due to their practical interest. This work examines the application of MAs to problems in these do- mains. We describe the basic architecture of a MA, and present some guidelines to the design of a successful MA for these applications. An overview of the existing literature on the topic is also provided. We conclude with some reflections on the lessons learned, and the future directions that research could take in this area.

A Memetic Algorithm for the Tool Switching Problem

This paper deals with the Tool Switching Problem (ToSP), a well-known problem in operations research. The ToSP involves determining a job sequence and the tools to be loaded on a machine with the goal of minimizing the total number of tool switches. This problem has been tackled by a number of algorithmic approaches in recent years. Here, we propose a memetic algorithm that combines a problem-specific permutational genetic algorithm with a hill-climbing procedure. It is shown that this combined approach outperforms each of the individual algorithms, as well as an ad-hoc beam search heuristic defined in the literature for this problem.