Learn More
Inductive Logic Programming (ILP) integrates the techniques from traditional machine learning and logic programming to construct logic programs from training examples. Most existing systems employ greedy search strategies which may trap the systems in a local maxima. This paper describes a system, called the Genetic Logic Programming System (GLPS), that(More)
This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of(More)
The self-organizing map (SOM) has been successfully employed to handle the Euclidean trav-eling salesman problem (TSP). By incorporating its neighborhood preserving property and the convex-hull property of the TSP, we introduce a new SOM-like neural network, called the expanding SOM (ESOM). In each learning iteration, the ESOM draws the excited neurons(More)
Genetic Programming (GP) and Inductive Logic Programming (ILP) have received increasing interest recently. Since their formalisms are so different, these two approaches cannot be integrated easily though they share many common goals and functionalities. A unification will greatly enhance their problem solving power. Moreover, they are restricted in the(More)
In this paper, we introduce a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population-based genetic algorithm (AEGA) to solve it. Working closely with an oncologist, we first modify the existing model, because its equation for the cumulative drug toxicity is inconsistent with medical knowledge and(More)
MOTIVATION Identification of transcription factor binding sites (TFBSs) plays an important role in deciphering the mechanisms of gene regulation. Recently, GAME, a Genetic Algorithm (GA)-based approach with iterative post-processing, has shown superior performance in TFBS identification. However, the basic GA in GAME is not elaborately designed, and may be(More)
In this paper, we propose a probabilistic rule-driven adaptive model (PRAM) for parameter adaptation and a repelling approach for diversity maintenance in genetic algorithms. PRAM uses three parameter values and a set of greedy rules to adapt the value of the control parameters automatically. The repelling algorithm is proposed to maintain the population(More)