Machine intelligence, adaptive business intelligence, and natural intelligence

Copyright © 2008 IEEEOne of the key observations of the author was that machine intelligence might be defined as the capability of a system to adapt its behavior to meet desired goals in a range of environments. Interestingly, the three components of prediction, adaptation, and optimization constitute the core modules of adaptive business intelligence systems. Clearly, the future of the business intelligence industry lies in systems that can make decisions, rather than tools that produce detailed reports.Zbigniew Michalewicz and Matthew Michalewic

Case study: An intelligent decision-support system

© 2005 IEEE.The explosive growth in decision-support systems over the past 30 years has yielded numerous "intelligent" systems that have often produced less-than-stellar results. In addition to generating data that users can't immediately apply to their tasks, such systems are often static, rendering them unable to respond to the dynamic nature of both business and the larger world. In this case study, the authors describe a thorny logistical problem: recommending the best distribution for used cars among various automobile auctions. They solved this problem by combining prediction, optimization, and adaptation techniques into one integrated system that has generated impressive profits for a large auto manufacturer.This article is part of a special issue on transportation and logistics.Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, and Constantin Chiria

Optimally Controlled Field-Free Orientation of the Kicked Molecule

Efficient and long-lived field-free molecular orientation is achieved using only two kicks appropriately delayed in time. The understanding of the mechanism rests upon a molecular target state providing the best efficiency versus persistence compromise. An optimal control scheme is referred to for fixing the free parameters (amplitudes and the time delay between them). The limited number of kicks, the robustness and the transposability to different molecular systems advocate in favor of the process, when considering its experimental feasibility.Comment: 5 pages, 2 figures (version 2 contains some minor additions and corrects many misprints

Using Entropy-Based Methods to Study General Constrained Parameter Optimization Problems

In this letter we propose the use of physics techniques for entropy determination on constrained parameter optimization problems. The main feature of such techniques, the construction of an unbiased walk on energy space, suggests their use on the quest for optimal solutions of an optimization problem. Moreover, the entropy, and its associated density of states, give us information concerning the feasibility of solutions.Comment: 10 pages, 3 figures, references correcte

La nostalgia del presente in Proust, Helleu e Boldini

Descrivendo in Le Côté de Guermantes un quadro del pittore immaginario Elstir, che corrisponde esattamente al celebre Dejeuner des canotiers di Renoir, Proust spiega che la “poetica dell’istante” caratteristica dell’impressionismo implica un “sensus finis” e quindi una nostalgia per il presente felice ma minacciato di morte. Questa chiave di lettura viene applicata anche al pittore francese Helleu (che per altri aspetti è anch’egli un modello di Elstir) e a Boldini, la cui “mondanità” è accompagnata da una percezione dolente della realtà

Coevolutionary optimization of fuzzy logic intelligence for strategic decision support

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We present a description and initial results of a computer code that coevolves fuzzy logic rules to play a two-sided zero-sum competitive game. It is based on the TEMPO Military Planning Game that has been used to teach resource allocation to over 20 000 students over the past 40 years. No feasible algorithm for optimal play is known. The coevolved rules, when pitted against human players, usually win the first few competitions. For reasons not yet understood, the evolved rules (found in a symmetrical competition) place little value on information concerning the play of the opponent.Rodney W. Johnson, Michael E. Melich, Zbigniew Michalewicz, and Martin Schmid

A competitive comparison of different types of evolutionary algorithms

This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulated annealing (RASA), the differential evolution (DE) in its original fashion developed by R. Storn and K. Price and simplified real-coded differential genetic algorithm (SADE). Each of these methods was developed for some specific optimization problem; namely the Chebychev trial polynomial problem, the so called type 0 function and two engineering problems - the reinforced concrete beam layout and the periodic unit cell problem respectively. Detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms. The results of our experiments suggest that the performance and robustness of RASA, IASA and SADE methods are comparable, while the DE algorithm performs slightly worse. This fact together with a small number of internal parameters promotes the SADE method as the most robust for practical use.Comment: 25 pages, 8 figures, 5 table

Time series forecasting for dynamic environments: The DyFor Genetic Program model

Copyright © 2007 IEEESeveral studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GP's potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.Neal Wagner, Zbigniew Michalewicz, Moutaz Khouja, and Rob Roy McGrego