Hitoshi Iba

Accelerating Differential Evolution Using an Adaptive Local Search

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

Particle swarm optimization with Gaussian mutation

In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal functions and multimodal functions. PSO with Gaussian mutation is able to obtain a result superior to GA. We also apply the PSO with Gaussian mutation to a gene network. Consequently, it has succeeded in acquiring better results than those by GA and PSO alone.

Inferring a system of differential equations for a gene regulatory network by using genetic programming

Describes an evolutionary method for identifying a gene regulatory network from the observed time series data of the genes expression. We use a system of ordinary differential equations as a model of the network and infer their right-hand sides by using genetic programming (GP). To explore the search space more effectively in the course of evolution, the least mean squares (LMS) method is used along with ordinary GP. We apply our method to three target networks and empirically show how successfully GP infers the systems of differential equations.

Genetic Programming 1998: Proceedings of the Third Annual Conference

Proceedings of the Annual Conferences on Genetic Programming. These proceedings present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, artificial life and evolution strategies, DNA computing, evolvable hardware, and genetic learning classifier systems.

Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics

We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time-series data of gene expression using the decoupled S-system formalism. We propose an Information Criteria-based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE)-based fitness evaluation. A hill-climbing local-search method has been incorporated in our evolutionary algorithm for efficiently attaining the skeletal architecture that is most frequently observed in biological networks. The suitability of the method is tested in gene circuit reconstruction experiments, varying the network dimension and/or characteristics, the amount of gene expression data used for inference, and the noise level present in expression profiles. The reconstruction method inferred the network topology and the regulatory parameters with high accuracy. Nevertheless, the performance is limited to the amount of expression data used and the noise level present in the data. The proposed fitness function has been found to be more suitable for identifying the correct network topology and for estimating the accurate parameter values compared to the existing ones. Finally, we applied the methodology for analyzing the cell-cycle gene expression data of budding yeast and reconstructed the network of some key regulators.

Enhancing differential evolution performance with local search for high dimensional function optimization

In this paper, we proposed Fittest Individual Refinement (FIR), a crossover based local search method for Differential Evolution (DE). The FIR scheme accelerates DE by enhancing its search capability through exploration of the neighborhood of the best solution in successive generations. The proposed memetic version of DE (augmented by FIR) is expected to obtain an acceptable solution with a lower number of evaluations particularly for higher dimensional functions. Using two different implementations DEfirDE and DEfirSPX we showed that proposed FIR increases the convergence velocity of DE for well known benchmark functions as well as improves the robustness of DE against variation of population. Experiments using multimodal landscape generator showed our proposed algorithms consistently outperformed their parent algorithms. A performance comparison with reported results of well known real coded memetic algorithms is also presented.

Using genetic programming to predict financial data

This paper presents the application of genetic programming (GP) to the prediction of price data in the Japanese stock market. The goal of this task is to choose the best stocks when making an investment and to decide when and how many stocks to sell or buy. There have been several applications of genetic algorithms (GAs) to financial problems, such as portfolio optimization, bankruptcy prediction, financial forecasting, fraud detection and scheduling. GP has also been applied to many problems in time-series prediction. However, relatively few studies have been made for the purpose of predicting stock market data by means of GP. This paper describes how successfully GP is applied to predicting stock data so as to gain a high profit. Comparative experiments are conducted with neural networks to show the effectiveness of the GP-based approach.

Evolutionary modeling and inference of gene network

This paper describes an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.

Evolvable Hardware and Its Applications to Pattern Recognition and Fault-Tolerant Systems

This paper describes Evolvable Hardware (EHW) and its applications to pattern recognition and fault-torelant systems. EHW can change its own hardware structure to adapt to the environment whenever environmental changes (including hardware malfunction) occur. EHW is implemented on a PLD(Programmable Logic Device)-like device whose architecture can be altered by re-programming the architecture bits. Through genetic algorithms, EHW finds the architecture bits which adapt best to the environment, and changes its hardware structure accordingly.

Inference of differential equation models by genetic programming

This paper describes an evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is well-known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs.