491232
A novel evolutionary drug scheduling model in cancer chemotherapy
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 clinical experience. To explore multiple efficient drug scheduling policies, we propose a novel variable representation-a cycle-wise representation, and modify the elitist genetic search operators in the AEGA. The simulation results obtained by the modified model match well with the clinical treatment experiences, and can provide multiple efficient solutions for oncologists to consider. Moreover, it has been shown that the evolutionary drug scheduling approach is simple, and capable of solving complex cancer chemotherapy problems by adapting multimodal versions of evolutionary algorithms
6017611
Balancing Samples' Contributions on GA Learning
A main branch in Evolutionary Computation is learning a system directly from input/output samples without investigating thternal behaviors of the system. Input/output samples captured from a real system are usually incomplete, biased and noisy. In order to evolve a precise system, the sample set should include a complete set of samples. Thus, a large number of samples should be used. Fitness functions being used in Evohitionary Algorithms usually based on the matched ratio of samples. Unfortunately, some of these samples may be exactly or semantically duplicated. These duplicated samples cannot be identified simply because we do not know the internal behavior of the system being evolved. This paper proposes a method to overcome this problem by using a dynamic fitness function that incorporates the contribution of each sample in the evolutionary process. Experiments on evolving Finite State Machines with Genetic Algorithms are presented to demonstrate the effect on improving the successful rate and convergent speed of the proposed method.
18388949
A probabilistic cooperative-competitive hierarchical model for global optimization
Abstract Stochastic searching methods have been applied widely to areas such as continuous and combinatorial optimization problems in a number of disciplines. Many existing methods solve these problems by navigating on the surface of the possibly rugged landscape. This kind of navigation is not very effective because the property of the landscape at different resolutions can be very different. Time spent at the beginning of the search on the detailed part of the landscape is often useless. Appropriate searching strategies should be adopted at different resolutions. In this paper, we propose a new probabilistic searching model for global optimization. The main contributions of the model are (1) to provide a basis for resolution control and smoothing of search space and (2) to introduce continuous memory into stochastic search. The basis of resolution control is achieved by dividing the search space into a finite number of n -dimensional partitions structurally. The number of partitions governs the resolution of the search space. The more the partitions, the finer is the search space and the more detailed and rugged is the landscape. The benefits are twofold. First, the rugged landscape problem can be smoothed, because the ruggedness is a matter of the number of partitions. Hence, the difficulty in search due to the ruggedness of the landscape can be controlled. Second, it provides a basis to implement algorithms that may change the ‘view’ of the landscape during the search process because we can dynamically divide the search space accordingly. Another important feature that we use is continuous memory . Throughout the search process, searching experience is continuously accumulated in order to shape the global picture of the search space guiding the future searching direction. We present results on the algorithm performance in handling numerical function optimization. The empirical results show that our new model is comparable to, and in many cases performs better than, that of the other advanced methods in terms of solution quality and computation required.
15319623
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
The Genetic Logic Programming System (GLPS) combines the learning power of genetic algorithms and the knowledge representation power of logic programming to overcome the shortcomings of existing paradigms. >
11001415
Automatic refinement of knowledge bases with fuzzy rules
Abstract The knowledge-acquisition bottleneck obstructs the development of expert systems. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem. The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II automatically. HERES employs heuristics and analytical methods to guide its generation of plausible refinements. The functionality and effectiveness of HERES are verified through various case studies. It has been verified that HERES can successfully refine knowledge bases. The refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms such as ID3 15–18 , AQ11, and INDUCE 14, 19, 20 because HERES' methods are currently unique in processing inexact examples and creating approximate rules.
5226759
Genetic Parallel Programming: Design and Implementation
This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential programif required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
5284748
A novel frequency compensation technique for low-voltage low-dropout regulator
A novel frequency compensation technique for low-voltage low-dropout regulator (LDR) is presented. The proposed technique, called pole-control frequency compensation (PCFC), provides faster loop response and does not require a large filtering capacitor. Both theoretical analysis and simulation show that the stability of the LDR using PCFC is independent of the load current, temperature and equivalent series resistance of the filtering capacitor. LDRs are well suited to portable equipment such as cellular phones, pagers and personal digital assistants.
2298581
Global Mobility Management by Replicated Databases in Personal Communication Networks
This paper explores the use of replicated databases for management of customer data (e.g., mobility data, call routing logic) in global, intelligent, and wireless networks. We propose and analyze two, full and partial, data replication schemes-which are compatible with industry protocol standards-and compare them with the traditional, centralized database scheme. By identifying a set of key teletraffic and mobility parameters, we develop a modeling framework based on queueing models and apply it to assess the relative performance and merits of these schemes. The paper also addresses some implementation issues. Numerical results reveal that the full replication scheme outperforms the centralized one over a wide range of the parameters considered in this study. Furthermore, if some customer data-such as location data for highly mobile customers in wireless networks-change frequently, and if each call launches multiple queries into the databases, the partial replication scheme offers further performance improvement. In general, however, the choice of the database design would depend on the specific characteristics of the service and user behavior under consideration.
588086
Calculating normalization constants of closed queueing networks by numerically inverting their generating functions
A new algorithm is developed for calculating normalization constants (partition functions) and moments of product-form steady-state distributions of closed queuing networks and related models. The essential idea is to numerically invert the generating function of the normalization constant and related generating functions appearing in expressions for the moments. It is known that the generating function of the normalization constant often has a remarkably simple form, but numerical inversion evidently has not been considered before. For p-dimensional transforms, as occur with queuing networks having p closed chains, the algorithm recursively performs p one-dimensional inversions. The required computation grows exponentially in the dimension, but the dimension can often be reduced by exploiting conditional decomposition based on special structure. For large populations, the inversion algorithm is made more efficient by computing large sums using Euler summation. The inversion algorithm also has a very low storage requirement. A key ingredient in the inversion algorithm is scaling. An effective static scaling is developed for multichain closed queuing networks with only single-server and (optionally) infinite-server queues. An important feature of the inversion algorithm is a self-contained accuracy check, which allows the results to be verified in the absence of alternative algorithms.
320235
Handling signature purposes in workflow systems
Abstract In paper-based workflow systems, signatures of individuals or groups of people have been used extensively for different purposes. Currently there are numerous studies on computerizing workflow systems. Also there are studies on implementing digital signatures in electronic media. But the diversified purposes of a signature in workflow makes a straightforward implementation of digital signature schemes inadequate. There are few studies on the implication of different signature purposes on electronic workflow systems. The purposes of signature are closely associated with the two modes of decision making, namely single and group. These two modes of decision making in turn lead to single and group signatures. This paper reports our comprehensive studies on signature purposes. These include the analysis of common signature purposes in workflow, classification of these purposes, classification of modes of decision making associated with these signature purposes, signing and validation requirements for handling these signature purposes, and finally an architecture to be incorporated in workflow engines for handling these signature purposes. In summary, this paper addresses a commonly neglected problem in information systems research: management control by signature in workflow.
39521573
An inversion algorithm for loss networks with state-dependent rates
We extend our recently developed algorithm for computing (exact) steady-state blocking probabilities for each class in product-form loss networks to cover general state-dependent arrival and service rates. This generalization allows us to consider, for the first time, a wide variety of buffered and unbuffered resource-sharing models with non-Poisson traffic as may arise with overflows in the context of alternative routing. As before, we consider non-complete-sharing policies involving upper-limit and guaranteed-minimum bounds for the different classes, but here we consider both bounds simultaneously. Major features of the algorithm are: dimension reduction by conditional decomposition based on special structure, an effective scaling algorithm to control errors in the inversion, the efficient treatments of multiple classes with identical parameters and the truncation of large sums.