An experimental comparative study for interactive evolutionary computation problems

Proceeding of: EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.This article has been financed by the Spanish founded research MCyT project OPLINK, Ref: TIN2006-08818-C04-02

Testing BOI and BOB algorithms for solving the Winner Determination

Eighth International Conference on Hybrid Intelligent Systems, 2008. HIS '08. Barcelona, 10-12 September 2008Combinatorial auctions are a promising auction format for allocating radio spectrum, as well as other goods. An important handicap of combinatorial auctions is determining the winner bids among many options, that is, solving the winner determination problem (WDP). This paper tackles this computational problem using two approaches in a combinatorial first-price sealed bid auction. The first one, is an A* based on items (BOI). The second one, is an A* based on bids (BOB). These two techniques are tested in several scenarios for allocating radio spectrum licenses. The results obtained reveal that the search algorithm A* with the BOB formulation outperforms the other and always finds the optimal solution very quickly

Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

Effects of a rationing rule on the ausubel auction: a genetic algorithm implementation

The increasing use of auctions as a selling mechanism has led to a growing interest in the subject. Thus both auction theory and experimental examinations of these theories are being developed. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this article is to develop a genetic algorithm to find automatically a bidder optimal strategy while the other players are always bidding sincerely. To this end a specific dynamic multiunit auction has been selected: the Ausubel auction, with private values, dropout information, and with several rationing rules implemented. The method provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments that differ in the elasticity of their demand curves, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding when rationing is needed.Publicad

Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.Publicad

Analysis of Ausubel auctions by means of evolutionary computation

IEEE Congress on Evolutionary Computation. Edimburgo, 2-5 September 2005The increasing use of auctions has led to a growing interest in the subject. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this paper is to develop a genetic algorithm to find bidders' optimal strategies for a specific dynamic multi-unit auction. The algorithm provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform canonical strategies

TV Series and Social Media: Powerful Engagement Factors in Mobile Video Games

The free-to-play business model has become hegemonic in the mobile video game industry, displacing the traditional paid content model that was the norm until the appearance of manufacturers’ app stores. Companies attempt to monetize these games by means of in-game micro-transactions and in-game advertising; thus, it is essential to acquire an enormous number of users because only a small percentage will ultimately make any purchases. To keep players engaged, companies typically put in place marketing and design strategies derived from behavioral telemetry, to maintain a grip on players. We propose an innovative approach, focusing our attention on the impact of having a video game based on a famous TV series. Furthermore, we analyze the effect of social networks on game metrics. The outcome indicates that developing a game based on a TV series and integrating social media with the gameplay improve and reinforce the user’s activation, retention and monetization

Modificaciones en la gestión del espectro: opciones y situación actual

El objetivo de este artículo es presentar las opciones que están considerándose (incluso ya aplicándose) para la reforma de los mecanismos de gestión del espectro radioeléctrico y evaluar su conveniencia y oportunidad. En concreto, se describen y valoran los tres cambios más profundos en este posible cambio de modelo: autorización del mercado secundario, utilización de la subasta para la asignación primaria y liberalización plena del uso del espectro. El proceso ya en marcha de introducción de estos cambios en España es también analizado