Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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Smooth Scheduling under Variable Rates or the Analog-Digital Confinement Game

This work considers non-terminating scheduling problems in which a system of multiple resources serves clients having variable needs. The system has m identical resources and n clients; in each time slot each resource may serve at most one client; in each such slot t each client γ has a rate, a real number ρ _γ (t), that specifies his needs in this slot. The rates satisfy the restriction ∑ _γ ρ _γ (t)≤m for any slot t. Except of this restriction, the rates can vary in arbitrary fashion. (This contrasts most prior works in this area in which the rates of the clients are constant.) The schedule is required to be smooth as follows: a schedule is Δ -smooth if for all time intervals I the absolute difference between the amount of service received by each client γ to his nominal needs of ∑ _t∈I ρ _γ (t) is less than Δ. Our objective are online schedulers that produce Δ-smooth schedules where Δ is a small constant which is independent of m and n. Our paper constructs such schedulers; these are the first online Δ-smooth schedulers, with a constant Δ, for clients with arbitrarily variable rates in a single or multiple resource system. Furthermore, the paper also considers a non-concurrent environment in which there is an additional restriction that each client is served at most once in each time slot; it presents the first online smooth schedulers for variable rates under this restriction. The above non-concurrent restriction is crucial in some applications (e.g., CPU scheduling). It has been pointed out that this restriction “adds a surprising amount of difficulty” to the scheduling problem. However, this observation was never formalized and, of course, was never proved. Our paper formalizes and proves some aspects of this observation. Another contribution of this paper is the introduction of a complete information, two player game called the analog-digital confinement game. In such a game pebbles are located on the real line; the two players, the analog player and the digital player, take alternating turns and each one, in his turn, moves some of the pebbles; the digital player moves the pebbles backwards by discrete distances while the analog player moves the pebbles forward by analog distances; the aim of the analog player is to cause one pebble (or more) to escape a pre-defined real interval while the aim of the digital player is to confine the pebbles into the interval. We demonstrate that this game is a convenient framework to study the general question of how to approximate an analog process by a digital one. All the above scheduling results are established via this game. In this derivation, the pebbles represent the clients, the analog player generates the needs of the clients and the digital player generates the schedule. Dedicated to the memory of Professor Shimon Even for his inspiration and encouragement     

Degree-Optimal Routing for P2P Systems

We define a family of Distributed Hash Table systems whose aim is to combine the routing efficiency of randomized networks—e.g. optimal average path length O(log ² n/δlog δ) with δ degree—with the programmability and startup efficiency of a uniform overlay—that is, a deterministic system in which the overlay network is transitive and greedy routing is optimal. It is known that Ω(log n) is a lower bound on the average path length for uniform overlays with O(log n) degree (Xu et al., IEEE J. Sel. Areas Commun. 22(1), 151–163, 2004). Our work is inspired by neighbor-of-neighbor (NoN) routing, a recently introduced variation of greedy routing that allows us to achieve optimal average path length in randomized networks. The advantage of our proposal is that of allowing the NoN technique to be implemented without adding any overhead to the corresponding deterministic network. We propose a family of networks parameterized with a positive integer c which measures the amount of randomness that is used. By varying the value c, the system goes from the deterministic case (c=1) to an “almost uniform” system. Increasing c to relatively low values allows for routing with asymptotically optimal average path length while retaining most of the advantages of a uniform system, such as easy programmability and quick bootstrap of the nodes entering the system. We also provide a matching lower bound for the average path length of the routing schemes for any c. This work was partially supported by the Italian FIRB project “WEB-MINDS” (Wide-scalE, Broadband MIddleware for Network Distributed Services), .     

A Randomized Algorithm for Online Unit Clustering

In this paper, we consider the online version of the following problem: partition a set of input points into subsets, each enclosable by a unit ball, so as to minimize the number of subsets used. In the one-dimensional case, we show that surprisingly the naïve upper bound of 2 on the competitive ratio can be beaten: we present a new randomized 15/8-competitive online algorithm. We also provide some lower bounds and an extension to higher dimensions.     

On the Black-Box Complexity of Sperner’s Lemma

We present several results on the complexity of various forms of Sperner’s Lemma in the black-box model of computing. We give a deterministic algorithm for Sperner problems over pseudo-manifolds of arbitrary dimension. The query complexity of our algorithm is linear in the separation number of the skeleton graph of the manifold and the size of its boundary. As a corollary we get an deterministic query algorithm for the black-box version of the problem 2D-SPERNER, a well studied member of Papadimitriou’s complexity class PPAD. This upper bound matches the deterministic lower bound of Crescenzi and Silvestri. The tightness of this bound was not known before. In another result we prove for the same problem an lower bound for its probabilistic, and an lower bound for its quantum query complexity, showing that all these measures are polynomially related. Research supported by the European Commission IST Integrated Project Qubit Application (QAP) 015848, the OTKA grants T42559 and T46234, and by the ANR Blanc AlgoQP grant of the French Research Ministry.     

Cryptographic and Physical Zero-Knowledge Proof Systems for Solutions of Sudoku Puzzles

We consider cryptographic and physical zero-knowledge proof schemes for Sudoku, a popular combinatorial puzzle. We discuss methods that allow one party, the prover, to convince another party, the verifier, that the prover has solved a Sudoku puzzle, without revealing the solution to the verifier. The question of interest is how a prover can show: (i) that there is a solution to the given puzzle, and (ii) that he knows the solution, while not giving away any information about the solution to the verifier. In this paper we consider several protocols that achieve these goals. Broadly speaking, the protocols are either cryptographic or physical. By a cryptographic protocol we mean one in the usual model found in the foundations of cryptography literature. In this model, two machines exchange messages, and the security of the protocol relies on computational hardness. By a physical protocol we mean one that is implementable by humans using common objects, and preferably without the aid of computers. In particular, our physical protocols utilize items such as scratch-off cards, similar to those used in lotteries, or even just simple playing cards. The cryptographic protocols are direct and efficient, and do not involve a reduction to other problems. The physical protocols are meant to be understood by “lay-people” and implementable without the use of computers. Research of R. Gradwohl was supported by US-Israel Binational Science Foundation Grant 2002246. Research of M. Naor was supported in part by a grant from the Israel Science Foundation. Research of B. Pinkas was supported in part by the Israel Science Foundation (grant number 860/06). Research of G.N. Rothblum was supported by NSF grant CNS-0430450 and NSF grant CFF-0635297.     

Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework

When evaluating transportation infrastructure projects and determining which of them will be carried out from a set of projects and given a budget constraint, several criteria need to be considered in the decision. Standard evaluation practices imply the aggregation of impacts into one utility function which is later optimized. Nevertheless these techniques used for translation of different measuring units into monetary terms are highly controversial. Multicriteria techniques can explicitly deal with different measuring units, however, they are not suitable to model interdependence relationships of projects that share a common characteristic (same route, location or target population, for instance). In this research we model this transportation planning problem, the multi-objective transportation infrastructure project selection problem (MTIPSP), as a constrained multi-objective optimization problem with quadratic objective functions, using a variation of the multi-objective 0–1 knapsack problem plus some additional constraints. Given the combinatorial nature of the problem, an evolutionary-based framework is used for the identification of Pareto solutions, and later, those with non-attractive properties are filtered using a Knee Identification Procedure. The final selection of the projects portfolio is made using a well known multicriteria decision aid method and including the decision makers’ preferences based on the existing context.