Volume 5, Number 1, 2008
Special Issue on Computational Intelligence in Enterprise (pp.1-99)
Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities.After these devices are plugged into a network,they initially advertise their presence and capabilities in the form of services so that they can be discovered and,if desired,exploited by the user or other networked devices.With the increasing number of these devices attached to the network,the complexity to configure and control them increases,which may lead to major processing and communication overhead.Hence,the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for,but also offer complex services that emerge from unique combinations of devices.This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming.However,with thissmart evolution,the cognitive load to configure and control such spaces becomes immense.One way to relieve this load is by employing artificial intelligence(AI)techniques to create an intelligentpresencewhere the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the users needs and behaviours.These AI mechanisms should be embedded in the users environments and should operate in a non-intrusive manner.This paper will show how computational intelligence(CI),which is an emerging domain of AI,could be employed and embedded in our living spaces to help such environments to be more energy efficient,intelligent,adaptive and convenient to the users.
This paper presents a new approach to the delineation of local labor markets based on evolutionary computation.The aim of the exercise is the division of a given territory into functional regions based on travel-to-work flows.Such regions are defined so that a high degree of inter-regional separation and of intra-regional integration in both cases in terms of commuting flows is guaranteed. Additional requirements include the absence of overlap between delineated regions and the exhaustive coverage of the whole territory. The procedure is based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size.In the experimentation stage,two variations of the fitness function are used,and the process is also applied as a final stage for the optimization of the results from one of the most successful existing methods,which are used by the British authorities for the delineation of travel-to-work areas(TTWAs).The empirical exercise is conducted using real data for a sufficiently large territory that is considered to be representative given the density and variety of travel-to-work patterns that it embraces.The paper includes the quantitative comparison with alternative traditional methods,the assessment of the performance of the set of operators which has been specifically designed to handle the regionalization problem and the evaluation of the convergence process.The robustness of the solutions,something crucial in a research and policy-making context,is also discussed in the paper.
This paper is motivated by the interest in finding significant movements in financial stock prices.However,when the number of profitable opportunities is scarce,the prediction of these cases is difficult.In a previous work,we have introduced evolving decision rules(EDR)to detect financial opportunities.The objective of EDR is to classify the minority class(positive cases)in imbalanced environments.EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities.The goals of this paper are:1)to show that EDR produces a range of solutions to suit the investors preferences and 2)to analyze the factors that benefit the performance of EDR.A series of experiments was performed.EDR was tested using a data set from the London Financial Market.To analyze the EDR behaviour,another experiment was carried out using three artificial data sets,whose solutions have different levels of complexity.Finally,an illustrative example was provided to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets.Experimental results show that:1)EDR offers a range of solutions to fit the risk guidelines of different types of investors,and 2)a bigger collection of rules is able to classify more positive cases in imbalanced environments.
The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers.In this paper,we introduce the motif tracking algorithm(MTA),a novel immune inspired(IS)pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data.The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs.Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist.The algorithm is tested using three separate data sets.Particular suitability to financial data is shown by applying it to oil price data.In all cases,the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilization of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.
The last few decades have seen a phenomenal increase in the quality,diversity and pervasiveness of computer games.The worldwide computer games market is estimated to be worth around USD 21bn annually,and is predicted to continue to grow rapidly. This paper reviews some of the recent developments in applying computational intelligence(CI)methods to games,points out some of the potential pitfalls,and suggests some fruitful directions for future research.
This paper compares two methods to predict inflation rates in Europe.One method uses a standard back propagation neural network and the other uses an evolutionary approach,where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results.However,the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal.Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.
Rationality is a fundamental concept in economics.Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we arebounded rational.However,it is very difficult to quantifybounded rationality,and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality.Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makersdecision-making procedures.This paper takes a computational point of view to Rubinsteins approach.From a computational point of view,decision procedures can be encoded in algorithms and heuristics.We argue that,everything else being equal,the effective rationality of an agent is determined by its computational power-we refer to this as the computational intelligence determines effective rationality(CIDER)theory.This is not an attempt to propose a unifying definition of bounded rationality.It is merely a proposal of a computational point of view of bounded rationality.This way of interpreting bounded rationality enables us to(computationally)reason about economic systems when the full rationality assumption is relaxed.
This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process.The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz,since portfolio managers often face a number of realistic constraints arising from business and industry regulations,while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders.For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint.An experimental study,based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations.The experimental results also illustrated its viability and practicality in handling the various realistic constraints.A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.
In this work,we explore and study the implication of having more than one output on a genetic programming(GP) graph-representation.This approach,called multiple interactive outputs in a single tree(MIOST),is based on two ideas.First,we defined an approach,called interactivity within an individual(IWI),which is based on a graph-GP representation.Second,we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this,we have MIOST.As a first step,we analyze the effects of IWI by using only mutations and analyze its implications(i.e.,presence of neutrality).Then,we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process.Finally,we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature.The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.
This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory(RST).The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions.It handles single-type and mixed attribute data sets with ease.The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.
A novel adaptive neural network(NN)output-feedback regulation algorithm for a class of nonlinear time-varying time- delay systems is proposed.Both the designed observer and controller are independent of time delay.Different from the existing results, where the upper bounding functions of time-delay terms are assumed to be known,we only use an NN to compensate for all unknown upper bounding functions without that assumption.The proposed design method is proved to be able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed system,and the system output is proved to converge to a small neighborhood of the origin.The simulation results verify the effectiveness of the control scheme.