Generalized Artificial Life Structure for Time-dependent Problems

In recent years, more attention has been paid on artificial life researches. Artificial life（AL） is a research on regulating gene parameters of digital organisms under complicated problematic environments through natural selections and evolutions to achieve the final emergence of intelligence. Most recent studies focused on solving certain real problems by artificial life methods, yet without much address on the AL life basic mechanism. The real problems are often very complicated, and the proposed methods sometimes seem too simple to handle those problems. This study proposed a new approach in AL research, named ＂generalized artificial life structure（GALS）＂, in which the traditional ＂gene bits＂ in genetic algorithms is first replaced by ＂gene parameters＂, which could appear anywhere in GALS. A modeling procedure is taken to normalize the input data, and AL ＂tissue＂ is innovated to make AL more complex. GALS is anticipated to contribute significantly to the fitness of AL evolution. The formation of ＂tissue＂ begins with some different AL basic cells, and then tissue is produced by the casual selections of one or several of these cells. As a result, the gene parameters, represented by ＂tissues＂, could become highly diversified. This diversification should have obvious effects on improving gene fitness. This study took the innovative method of GALS in a stock forecasting problem under a carefully designed manipulating platform. And the researching results verify that the GALS is successful in improving the gene evolution fitness.

Interaction model of artificial fish in virtual environment

Conventional artificial fish has some shortages on the interaction with environment, other fish, and the animator. This article proposes a multi-tier interaction control model of artificial fish, realizes the interaction model through integration of virtual reality technology and Markov sequence, and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator. Simulation results show that the interaction model owns not only the basic characteristics of virtual biology, but also has high trueness interaction function.

Pattern formation in mutation of “Game of Life”

This paper presents pattern formation in generalized cellular automata (GCA) by varying parameters of classic “game of life”. Different dynamic behaviors are classified. The influence of remembrance of dynamic behavior of GCA is also studied. Experiments show the emergence of the self-organizing patterns that is analogous with life forms at the edge of chaos, which consist of certain nontrivial structure and go through periods of growth, maturity and death. We describe these experiments and discuss their potential as alternative way for creating artificial life and generative art, and as a new method for pattern genesis.

Colony location algorithm for assignment problems

A novel algorithm called Colony Location Algorithm (CLA) is proposed. It mimics the phenomena in biotic community that colonies of species could be located in the places most suitable to their growth. The factors working on the species location such as the nutrient of soil, resource competition between species, growth and decline process, and effect on environment were considered in CLA via the nutrient function, growth and decline rates, environment evaluation and fertilization strategy. CLA was applied to solve the classical assignment problems. The computation results show that CLA can achieve the optimal solution with higher possibility and shorter running time.

Mindless Intelligence Method for Solving the Tower of Hanoi Problem

Over the past years, more and more attention has been paid to artificial life research. The main object of artificial life research is to explore how to control the environments in which the digital organisms imitating natural life, under complicated competition and evolutionary conditions, develop their own wisdom, which can then be used to solve the problems in the real world. While most of the current researches applied one or another artificial life method to solve real problems, the fundamental mechanism of the emerging process of artificial life is seldom addressed. The research works on genetic algorithms, although bearing fruitful results, could only be deemed as constituting a basic stage in the process of artificial life development. This study proposes a new method of employing artificial life, to complement the contents of the research of mindless intelligence, which is regarded as a bridge linking genetic algorithms to general artificial life. And two important concepts, key manipulating parameters and contribution function in its context, are proposed to expand the mindless intelligence applications, in order to pave the way for the optimal design of an artificial life method, in an attempt to fill the conceptual gap between genetic algorithms and artificial life, and consequently clarifying the artificial life mechanism. As a case study we applied these innovative methods to solve an open problem： the Tower of Hanoi, to attest to the feasibility of our approach, and we have achieved satisfactory results.

On the Behavior Characteristics of Cellular Automata

In this paper, the inherent rdationships between the running regulations and behavior charactcristics of cellular automata are presented; an imprecise taxonomy of such syseros is put forward; the three extreme cases of slablc sysems are discussed; and the illogicalness of evolutional strategies of cellular automata is analyzed. The result is suilable for the emulation and prediction of behavior of discrete dynamics sys terns; especially it can be taken as an important analysis means of dynamic performance of complex networks.

Research on food-chain algorithm and its parameters

Based on the characteristics of colony emer-gence of artificial organisms,their dynamic interaction with the environment,and the food-chain crucial to the life system,the rules of local activities of artificial organ-isms at different levels are defined.The article proposes an artificial life-based algorithm,which is referred to as the food-chain algorithm.This algorithm optimizes computa-tion by simulating the evolution of natural ecosystems and the information processing mechanism of natural organ-isms.The definition,idea and flow of the algorithm are introduced,and relevant rules on metabolic energy and change in the surroundings where artificial-life individuals live are depicted.Furthermore,key parameters of the algorithm are systematically analyzed.Test results show that the algorithm has quasi-life traits that include being autonomous,evolutionary,and self-adaptive.These traits are highly fit for optimization problems of life-like sys-tems such as the location-allocation problem of a distri-bution network system.