Catálogo de publicaciones - libros

We studied the evolution of cooperation on social networks based on personal reputation using random pairing rule. Small-world networks and scale-free networks are used as practical network model. The iterated prisoner’s dilemma game are adopted as theorotical tool in which players are paired according to the network structure to play the ONE-SHOT prisoner’s dilemma game. Computer simulation shows that TIT-FOR-TAT-like strategy pattern will emerge from initial enviroments and cooperation can be maintained even in social networks when players have little chance to play continuous repeated games.

In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by the stocks selected by GA is used as evaluation criterion. Experiment results reveal that the proposed GA for stock selection provides a very flexible and useful tool to assist the investors in selecting valuable stocks.

This paper examines stock prices forecasting and trading strategies’ development with means of computational intelligence (CI), addressing the issue of an artificial neural network (ANN) topology dependency.

Simulations reveal optimal network settings. Optimality of discovered ANN topologies’ is explained through their links with the ARMA processes, thus presenting identified structures as nonlinear generalizations of such processes. Optimal settings examination demonstrates the weak relationships between statistical and economic criteria.

The research demonstrates that fine-tuning ANN settings is an important stage in the computational model set-up for results’ improvement and mechanism understanding. Genetic algorithm (GA) is proposed to be used for model discovery, making technical decisions less arbitrary and adding additional explanatory power to the analysis of economic systems with CI.

The paper is a step towards the econometric foundation of CI in finance. The choice of evaluation criteria combining statistical and economic qualities is viewed as essential for an adequate analysis of economic systems.

In this research, an agent-based simulation model for price competition in oligopolies is built and Genetic Algorithm is used to evolve the oligopolies’ decisions of price while facing the competitors in markets. The experimental results show two factors influencing the price competition situations and ‘given’ factor that competitor can not control leads strong influence on their decision of price. Total cooperation (Collusion to high prices) seems not to be achieved under the different parameter settings while many competitors involving in the market and a limitation of cooperation forms in which no effect forces the achievement of total cooperation.

In this work, a method is presented for analysis of Markov chains modeling evolutionary algorithms through use of a suitable quotient construction. Such a notion of quotient of a Markov chain is frequently referred to as “coarse graining” in the evolutionary computation literature. We shall discuss the construction of a quotient of an irreducible Markov chain with respect to an arbitrary equivalence relation on the state space. The stationary distribution of the quotient chain is “coherent” with the stationary distribution of the original chain. Although the transition probabilities of the quotient chain depend on the stationary distribution of the original chain, we can still exploit the quotient construction to deduce some relevant properties of the stationary distribution of the original chain. As one application, we shall establish inequalities that describe how fast the stationary distribution of Markov chains modelling evolutionary algorithms concentrates on the uniform populations as the mutation rate converges to 0. Further applications are discussed.

Many important fitness functions in Evolutionary Computation (EC) have high degree of neutrality i.e. large regions of the search space with identical fitness. However, the impact of neutrality on the runtime of Evolutionary Algorithms (EAs) is not fully understood. This work analyses the impact of the accessibility between neutral networks on the runtime of a simple randomised search heuristic. The runtime analysis uses a connection between random walks on graphs and electrical resistive networks.

The Iterated Prisoners Dilemma (IPD) has received much attention because of its ability to demonstrate altruistic behavior. However, most studies focus on the synchronous case, where players make their decisions simultaneously. As this is implausible in most biological contexts, a more generalized approach is required to study the emergence of altruistic behavior in an evolutionary context. Here, we take previous results and present a generalized Markov model for asynchronous IPD, where both, one, or neither player can make a decision at a given time step. We show that the type of asynchronous timing introduced into the model influences the strategy that dominates. The framework presented here is a more biologically plausible scenario through which to investigate altruistic behavior.

Two independent component analysis (ICA) algorithms are applied for blind source separation (BSS) in a synthetic, multi-sensor situation, within a non-destructive pipeline test. CumICA is based in the computation of the cross-cumulants of the mixtures and needs the aid of a digital high-pass filter to achieve the same SNR (up to –40 ) as Fast-ICA. Acoustic Emission (AE) sequences were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Four common sources in AE testing are linearly mixed, involving real AE sequences, impulses and parasitic signals modelling human activity.

In high bit rate optical fiber communication systems, Polarization mode dispersion (PMD) is one of the main factors to signal distortion and needs to be compensated. Because PMD possesses the time-varying and statistical properties, to establish an effective control algorithm for adaptive or automatic PMD compensation is a challenging task. Widely used control algorithms are the gradient-based peak search methods, whose main drawbacks are easy being locked into local sub-optima for compensation and no ability to resist noise. In this paper, we introduce a new evolutionary approach, particle swarm optimization (PSO), into automatic PMD compensation as feedback control algorithm. The experiment results showed that PSO-based control algorithm had unique features of rapid convergence to the global optimum without being trapped in local sub-optima and good robustness to noise in the transmission line that had never been achieved in PMD compensation before.

A central question in cell and developmental biology is how signaling pathways maintain specificity and avoid erroneous cross-talk so that distinct signals produce the appropriate changes. In this paper, a model system of the yeast mating, invasive growth and stress-responsive mitogen activated protein kinase (MAPK) cascades for scaffolding-mediated is developed. Optimization with respect to the mutual specificity of this model system is performed by a high performance multi-objective evolutionary algorithm (HPMOEA) based on the principles of the minimal free energy in thermodynamics. The results are good agreement with published experimental data. (1) Scaffold proteins can enhance specificity in cell signaling when different pathways share common components; (2) The mutual specificity could be accomplished by a selectively-activated scaffold that had a relatively high value of dissociation constant and reasonably small values of leakage rates; (3) When Pareto-optimal mutual specificity is achieved, the coefficients, deactivation rates reach fastest, association and leakage rates reach slowest.