Vincenzo Cutello

Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators

In this paper, an important class of hypermutation operators are discussed and quantitatively compared with respect to their success rate and computational cost. We use a standard Immune Algorithm (IA), based on the clonal selection principle to investigate the searching capability of the designed hypermutation operators. We computed the parameter surface for each variation operator to predict the best parameter setting for each operator and their combination. The experimental investigation in which we use a standard clonal selection algorithm with different hypermutation operators on a complex “toy problem”, the trap functions, and a complex NP-complete problem, the 2D HP model for the protein structure prediction problem, clarifies that only few really different and useful hypermutation operators exist, namely: inversely proportional hypermutation, static hypermutation and hypermacromutation operators. The combination of static and inversely proportional Hypermutation and hypermacromutation showed the best experimental results for the “toy problem” and the NP-complete problem.

Clonal selection algorithms: a comparative case study using effective mutation potentials

This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.

Fuzzy classification systems

In this paper it is pointed out that a classification is always made taking into account all the available classes, i.e., by means of a classification system. The approach presented in this paper generalizes the classical definition of fuzzy partition as defined by Ruspini, which is now conceived as a quite often desirable objective that can be usually obtained only after a long learning process. In addition, our model allows the evaluation of the resulting classification, according to several indexes related to covering, relevance and overlapping.

A multi-objective evolutionary approach to the protein structure prediction problem

The protein structure prediction (PSP) problem is concerned with the prediction of the folded, native, tertiary structure of a protein given its sequence of amino acids. It is a challenging and computationally open problem, as proven by the numerous methodological attempts and the research effort applied to it in the last few years. The potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms). In this paper, we show experimentally that those types of interactions are in conflict, and do so by using the potential energy function Chemistry at HARvard Macromolecular Mechanics. A multi-objective formulation of the PSP problem is introduced and its applicability studied. We use a multi-objective evolutionary algorithm as a search procedure for exploring the conformational space of the PSP problem.

Recursive connective rules

An associative binary connective allows the evaluation of arbitrary finite sequences of items by means of a one‐by‐one sequential process. In this paper we develop an alternative approach for those nonassociative connectives, allowing a sequential definition by means of binary fuzzy connectives. It will be then stressed that a connective rule should be understood as a consistent sequence of binary connective operators. ©1999 John Wiley & Sons, Inc.

Parallel Problem Solving from Nature - PPSN XII

The information geometric optimization (IGO) flow has been introduced recently by Arnold et al. This distinguished mathematical flow on the parameter manifold of a family of search distributions constitutes a novel approach to the analysis of several randomized search heuristics, including modern evolution strategies. Besides its appealing theoretical properties, it offers the unique opportunity to approach the convergence analysis of evolution strategies in two independent steps. The first step is the analysis of the flow itself, or more precisely, the convergence of its trajectories to Dirac peaks over the optimum. In a second step it remains to study the deviation of actual algorithm trajectories from the continuous flow. The present study approaches the first problem. The IGO flow of isotropic Gaussian search distributions is analyzed on convex, quadratic fitness functions. Convergence of all trajectories to the Dirac peak over the optimum is established.

An Immunological Approach to Combinatorial Optimization Problems

In this work we use a simplified model of the immune system to explore the problem solving feature. We consider only two immunological entities, antigens and antibodies, two parameters, and simple immune operators. The experimental results shows how a simple randomized search algorithm coupled with a mechanism for adaptive recognition of hardest constraints, is sufficient to obtain optimal solutions for any combinatorial optimization problem.

Fuzzy rationality measures

Fuzzy preference relations formalize intensity of individual preferences over fixed sets of alternatives. It is therefore natural to extend to fuzzy preferences the notion of rationality or consistency. With this goal in mind, we address in this paper the problem of giving an axiomatic basis for defining the concept of fuzzy rationality. Specifically, we establish a collection of conditions that any fuzzy rationality measure must satisfy. Some examples of fuzzy rationality measures are then given and analyzed.

A hybrid immune algorithm with information gain for the graph coloring problem

We present a new Immune Algorithm that incorporates a simple local search procedure to improve the overall performances to tackle the graph coloring problem instances. We characterize the algorithm and set its parameters in terms of Information Gain. Experiments will show that the IA we propose is very competitive with the best evolutionary algorithms.

Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator

Numerical optimization of given objective functions is a crucial task in many real-life problems. This paper introduces a new immunological algorithm for continuous global optimization problems, called opt-IMMALG; it is an improved version of a previously proposed clonal selection algorithm, using a real-code representation and a new Inversely Proportional Hypermutation operator.We evaluate and assess the performance of opt-IMMALG and several others algorithms, namely opt-IA, PSO, arPSO, DE, and SEA with respect to their general applicability as numerical optimization algorithms. The experiments have been performed on 23 widely used benchmark problems.The experimental results show that opt-IMMALG is a suitable numerical optimization technique that, in terms of accuracy, outperforms the analyzed algorithms in this comparative study. In addition it is shown that opt-IMMALG is also suitable for solving large-scale problems.