Exploring Millions of 6-State FSSP Solutions: the Formal Notion of Local CA Simulation

In this paper, we come back on the notion of local simulation allowing to transform a cellular automaton into a closely related one with different local encoding of information. This notion is used to explore solutions of the Firing Squad Synchronization Problem that are minimal both in time (2n -- 2 for n cells) and, up to current knowledge, also in states (6 states). While only one such solution was proposed by Mazoyer since 1987, 718 new solutions have been generated by Clergue, Verel and Formenti in 2018 with a cluster of machines. We show here that, starting from existing solutions, it is possible to generate millions of such solutions using local simulations using a single common personal computer

A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems

On the definition of dynamic permutation problems under landscape rotation.

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In the case of dynamic permutation problems, this process has been usually done by the rotation of the landscape. This technique modifies the encoding of the problem and maintains its structure over time. Commonly, the changes are performed based on the previous state, recreating a concatenated changing problem. However, despite its simplicity, our intuition is that, in general, the landscape rotation may induce severe changes that lead to problems whose resemblance to the previous state is limited, if not null. Therefore, the problem should not be classified as a DOP, but as a sequence of unrelated problems. In order to test this, we consider the flow shop scheduling problem (FSSP) as a case study and the rotation technique that relabels the encoding of the problem according to a permutation. We compare the performance of two versions of the state-of-the-art algorithm for that problem on a wide experimental study: an adaptive version that benefits from the previous knowledge and a restarting version. Conducted experiments confirm our intuition and reveal that, surprisingly, it is preferable to restart the search when the problem changes even for some slight rotations. Consequently, the use of the rotation technique to recreate dynamic permutation problems is revealed in this work

Adaptation and parameters studies of CS algorithm for flow shop scheduling problem

Scheduling concerns the allocation of limited resources overtime to perform tasks to fulfill certain criterion and optimize one or several objective functions. One of the most popular models in scheduling theory is that of the flow-shop scheduling. During the last 40 years, the permutation flow-shop sequencing problem with the objective of makespan minimization has held the attraction of many researchers. This problem characterized as Fm/prmu/Cmax in the notation of Graham, involves the determination of the order of processing of n jobs on m machines. In addition, there was evidence that m-machine permutation flow-shop scheduling problem (PFSP) is strongly NP-hard for m ≥3. Due to this NP-hardness, many heuristic approaches have been proposed, this work falls within the framework of the scientific research, whose purpose is to study Cuckoo search algorithm. Also, the objective of this study is to adapt the cuckoo algorithm to a generalized permutation flow-shop problem for minimizing the total completion time, so the problem is denoted as follow: Fm | | Cmax. Simulation results are judged by the total completion time and algorithm run time for each instance processed

MFCS\u2798 Satellite Workshop on Cellular Automata

For the 1998 conference on Mathematical Foundations of Computer Science (MFCS\u2798) four papers on Cellular Automata were accepted as regular MFCS\u2798 contributions. Furthermore an MFCS\u2798 satellite workshop on Cellular Automata was organized with ten additional talks. The embedding of the workshop into the conference with its participants coming from a broad spectrum of fields of work lead to interesting discussions and a fruitful exchange of ideas. The contributions which had been accepted for MFCS\u2798 itself may be found in the conference proceedings, edited by L. Brim, J. Gruska and J. Zlatuska, Springer LNCS 1450. All other (invited and regular) papers of the workshop are contained in this technical report. (One paper, for which no postscript file of the full paper is available, is only included in the printed version of the report). Contents: F. Blanchard, E. Formenti, P. Kurka: Cellular automata in the Cantor, Besicovitch and Weyl Spaces K. Kobayashi: On Time Optimal Solutions of the Two-Dimensional Firing Squad Synchronization Problem L. Margara: Topological Mixing and Denseness of Periodic Orbits for Linear Cellular Automata over Z_m B. Martin: A Geometrical Hierarchy of Graph via Cellular Automata K. Morita, K. Imai: Number-Conserving Reversible Cellular Automata and Their Computation-Universality C. Nichitiu, E. Remila: Simulations of graph automata K. Svozil: Is the world a machine? H. Umeo: Cellular Algorithms with 1-bit Inter-Cell Communications F. Reischle, Th. Worsch: Simulations between alternating CA, alternating TM and circuit families K. Sutner: Computation Theory of Cellular Automat