Implications of the ability to learn simple actions on the efficiency of the evolution of social behavior of agents

We investigate the effect of the ability to learn simple actions on the performance characteristics of the evolution of social behavior in agents situated in an inherently cooperative environment. Using a continuous predator–prey pursuit problem, we verified that relatively complex social behavior emerges from simple, implicit, locally defined, and thus robust and scalable interactions between the predator agents. Considering a distinct aspect of the phenomenon of emergence, we hypothesize that the ability of agents to learn how to perform simple, atomic acts of implicit interaction might facilitate the evolution of more complex behavior. The empirical results indicate that incorporation of the proposed approach to learning in genetic programming (employed as an algorithmic paradigm to evolve the social behavior of the agents) is associated with about a two-fold decrease in the computational effort of evolution.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

基于XML技术实现C/S异构数据库的集成

基于现有的XML技术,本文提出了一种实现异构数据库集成的新方案。文中描述了在此方案中系统实际的工作流程,并对其核心模块的实现细节进行了理论分析。最后给出了基于此系统模型上用户访问数据库所需的工作流程。     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.     

Synergies of simulation, agents, and systems engineering

Simulation, software agents, and systems engineering are three important disciplines; each of which support many application areas. In this article it is pointed out that their usefulness and efficacy can be significantly improved by first and higher order synergies.     

Comparing extended classifier system and genetic programming for financial forecasting: an empirical study

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP.     

A study of evolution strategy based cooperative behavior in collective agents

The following paper introduces an evolution strategy on the basis of cooperative behaviors in each group of agents. The evolution strategy helps each agent to be self-defendable and self-maintainable. To determine an optimal group behavior strategy under dynamically varying circumstances, agents in same group cooperate with each other. This proposed method use reinforcement learning, enhanced neural network, and artificial life. In the present paper, we apply two different reward models: reward model 1 and reward model 2. Each reward model is designed as considering the reinforcement or constraint of behaviors. In competition environments of agents, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained as subtracting the values. And we propose an enhanced neural network to add learning behavior of an artificial organism-level to artificial life simulation. In future, the system models and results described in this paper will be applied to the framework of healthcare systems that consists of biosensors, healthcare devices, and healthcare system.     

Active learning of expressive linkage rules using genetic programming

A central problem in the context of the Web of Linked Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and expertise required to write linkage rules, we present the ActiveGenLink algorithm which combines genetic programming and active learning to generate expressive linkage rules interactively. The ActiveGenLink algorithm automates the generation of linkage rules and only requires the user to confirm or decline a number of link candidates. ActiveGenLink uses a query strategy which minimizes user involvement by selecting link candidates which yield a high information gain. Our evaluation shows that ActiveGenLink is capable of generating high quality linkage rules based on labeling a small number of candidate links and that our query strategy for selecting the link candidates outperforms the query-by-vote-entropy baseline.     

Generative learning of visual concepts using multiobjective genetic programming

This paper introduces a novel method of visual learning based on genetic programming, which evolves a population of individuals (image analysis programs) that process attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognizes the training concept (shape). The approach uses generative evaluation scheme: individuals are rewarded for reproducing the shape of the object being recognized using graphical primitives and elementary background knowledge encoded in predefined operators. Evolutionary run is driven by a multiobjective fitness function to prevent premature convergence and enable effective exploration of the space of solutions. We present the method in detail and verify it experimentally on the task of learning two visual concepts from examples.     

遗传规划算法在化合物设计、筛选研究中的应用

采用计算机科学中新兴的遗传规划算法思想，结合化学物质的本质特点，运用进化操作来实现化合物的合成设计和筛选。文中针对算法运用讨论了函数集、终止集问题，通过计算元素组成的字符串的化合价的结果来确定适应度函数，既符合化学学科的本质规律，又满足了算法的要求。通过复制、交换和突变操作，经过多代次的进化终止，取得了满意的结果。文章还针对其实用性，从化学本质出发，提出了建议和研究方向。可以说本文是遗传规划在化学化合物合成筛选中运用的成功探索，同时也为进一步研究打下了坚实的基础。     

Modeling of arsenic, chromium and cadmium removal by nanofiltration process using genetic programming

In this paper, genetic programming (GP) as a novel approach for the explicit formulation of nanofiltration (NF) process performance is presented. The objective of this study is to develop robust models based on experimental data for prediction the membrane rejection of arsenic, chromium and cadmium ions in a NF pilot-scale system using GP. Feed concentration and transmembrane pressure were considered as input parameters of the models. The ions rejection is considered as output parameter of the models. Some statistical parameters were considered and calculated in order to investigate the reliability of each model. The results showed quite satisfactory accuracies of the proposed models based on GP. The results also nominated GP as a potential tool for identifying the behavior of a NF system.     

The structure of the genetic programming collaboration network

The genetic programming bibliography aims to be the most complete reference of papers on genetic programming. In addition to locating publications, it contains coauthor and coeditor relationships which have not previously been studied. These reveal some similarities and differences between our field and collaborative social networks in other scientific fields.

基于XML远程教学资源管理系统的研究与应用

为解决学习内容的合理组织所提出的知识体系结构的构建,以及知识体系结构与元数据的关系,研究了媒体素材库的学习对象元数据。根据学习内容的组织,结合有关标准规范,介绍了一个具体的远程教育资源管理系统的功能模块和结构,并讨论了用于系统开发的XML及其他相关技术。     

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.     

基于XML的远程教育资源描述和管理系统的设计

文中简要介绍了XML在远程教育资源描述和管理方面的优势,给出了一个基于XML远程教育系统中资源开发和管理系统的结构设计,并在此设计的基础上详细讨论了XML在资源的描述、个性化显示、交互、更新和访问方面的应用。     

Combined strength of holons,agents and function blocks in cyber-physical systems

This paper presents a novel approach to implementing cyber-physical systems (CPS) using the combined strength of holons, agents and function blocks. Within the context, a CPS is represented by a holarchy of multiple holons. Each holon possesses a logical part and a physical part, which mimic the cyber and physical entities of the CPS. During implementation, the two parts of a holon are realised by agents and function blocks for information processing and materials processing, respectively. The objective of this research is to provide a concept map and associate a CPS with holons, agents and function blocks for the ease of system implementation in decentralised or cloud environment.     

Automatic offloading of mobile applications into the cloud by means of genetic programming

The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process.     

A genetic approach to the design of autonomous agents for futures trading

We propose a genetic algorithm-based method for designing an autonomous trader agent. The task of the proposed method is to find an optimal set of fuzzy if–then rules that best represents the behavior of a target trader agent. A highly profitable trader agent is used as the target in the proposed genetic algorithm. A trading history for the target agent is obtained from a series of futures trading. The antecedent part of fuzzy if–then rules considers time-series data of spot prices, while the consequent part indicates the order of trade (Buy, Sell, or No action) with its degree of certainty. The proposed method determines the antecedent part of fuzzy if–then rules. The consequent part of fuzzy if–then rules is automatically determined from the trading history of the target trader agent. The autonomous trader agent designed by the proposed genetic algorithm consists of a fixed number of fuzzy if–then rules. The decision of the autonomous trader agent is made by fuzzy inference from the time-series data of spot prices. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006