The Scientific World Journal

Volume 2015 (2015), Article ID 704587, 14 pages

http://dx.doi.org/10.1155/2015/704587

## From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

Computer Science Division, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima 965-8580, Japan

Received 28 July 2014; Revised 11 October 2014; Accepted 12 November 2014

Academic Editor: Albert Victoire

Copyright © 2015 Yan Pei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

#### 1. Introduction

Philosophy of determinism comes from the development of classic mechanic that was originally established and studied by Isaac Newton, Pierre-Simon Laplace, Gottfried Wilhelm Leibniz, and so forth. Strict determinism indicates that causality can be expressed and implemented by mathematical calculation and logical reasoning. As Pierre-Simon Laplace said, “we may regard the present state of the universe as the effect of its past and the cause of its future” [1]. In the philosophy of determinism, everything is deterministic and predictable. However, the discovery of probability breaks dominated position of determinism in scientific philosophy. Particularly, the proposal of law of larger numbers extends the recognition scale of science, which explains the relationship between probability and frequency. The two philosophies and methodologies, determinism and probability, dominate researches in science until the discovery of chaos, another philosophy and methodology which can present and explain the natural world. In optimization field, deterministic and stochastic optimization algorithms are theoretically supported by philosophy and methodology of determinism and probability. However, because some fundamental works of chaos theory are not completed yet, research and development of chaotic optimization algorithm are still rarely mentioned and studied in optimization field. This paper tries to present a limit work on chaotic optimization algorithm with evolution concept.

Most of evolutionary computation (EC) algorithms are inspired from natural phenomena, such as genetic algorithm that mimics the process of natural selection and survival of the fittest. EC can be involved in continuous and combinational optimization problems, and its algorithm has a metaheuristic or stochastic characteristic in their search mechanisms [2]. As EC algorithms have been developed further and deeply, not only biological phenomena but also physical and mathematical phenomena and mechanisms are introduced into EC area to implement new EC algorithms. This research subject is one of computational paradigm studies in natural computing area and enriches research context of computational intelligence. In a viewpoint of EC search scheme, its algorithms encompass two components in their search mechanisms. One is a search methodology by a variety of implementations, and the other is an iterative process to simulate evolution. Most of EC algorithms do not require the optimization problem to have some specific characteristics, and few of them utilize any mathematical properties or mechanisms to ensure convergence of the algorithms. If we introduce mathematical optimization property or mechanism that simulates the natural phenomena, such as chaotic ergodicity, into an optimization iterative process, we may implement new EC algorithms that partially ensure their convergence. The hint and philosophy behind this motivation benefit are to improve the global convergence characteristic of the algorithm.

The study on chaos theory comes from the real problems of physics, ecology, and mathematics since three-body problem was studied by Poincare et al. [3, 4]. It is developed originally to describe the system behaviour that cannot be described either by deterministic system or by stochastic system, which enriches our knowledge on unpredictable property of natural system. That means the system cannot be normalized by a set of differential equations or probability density function. Chaos and chaotic system have many characteristics for implementing an evolutionary search prototype and framework and improving EC algorithms. Nonlinearity, ergodicity, and sensitiveness are major explicit properties of chaos and chaotic system. The ergodicity of chaotic system can support more diversity to enhance exploration and exploitation capability of EC algorithms. It can approach to any desired point in search space with arbitrary accuracy and movement track. The sensitiveness of initial condition of chaotic system can lead to different search paths and escape from local optima for enhancing search performance of EC algorithms. The motion of perturbation around chaotic attractor can be simulated as an optimization search scheme and framework by EC.

There are typically three applications in which chaos is used in optimization area, that is, a local search method, a parameter tuning technique, and the new EC algorithm inspiration resource. Some chaotic systems are introduced into conventional EC algorithm frameworks to make population diversity in local search and to tune the parameters of algorithm [5]. The ergodicity property and behaviour are modelled as a search prototype to make a new EC algorithm, such as chaotic evolution optimization framework [6]. Chaotic Krill Herd algorithm was proposed by fusing Krill Herd algorithm and chaos theory [7], and some of its improved versions were proposed and studied [8, 9].

Chaotic evolution (CE) is a population-based algorithm framework that simulates chaotic motion behaviour in a search space [6]. Because of the ergodicity of chaotic system, chaotic motion can visit any point with arbitrary accuracy. CE algorithms take into account ergodicity property in its search mechanism to implement a new optimization scheme. There are three parameters as algorithm setting in its framework, a chaotic system and its parameter(s), a direction rate to guide the percentage of search direction, and a crossover rate. The chaotic system supports a basic simulation parameter, chaotic parameter, to implement the search function. Parameter of the chaotic system is simply set at the value that can lead to a chaotic output of the system. The direction rate decides search direction of each individual in CE algorithm, which is usually set at a random value. The crossover operation reserves extensibility of CE algorithm for further study, which can be set at 100%. It means that the mutant vector of CE algorithm can be directly presented as the chaotic vector of CE algorithm. For an empirical study, it is not a necessary parameter in CE optimization framework [6]. However, we cannot deny its effectiveness in theory from our current best knowledge.

This paper extends the work of [6] by introducing new chaotic systems into the CE algorithm framework and conducts a comparison study with DE algorithms. We use 25 benchmark functions with 10- and 30-dimensional setting to evaluate our new designed CE algorithms. We also establish a new interactive EC (IEC) algorithm by using CE optimization framework and evaluate its performance by using a pseudo-IEC user, that is, a Gaussian mixture model. Some statistical tests, such as Wilcoxon sign-ranked test, Friedman test, and Bonferroni-Dunn’s test, are applied to evaluate significance and ranking of the algorithms in the related experiments and discussions. This work does not pursue obtaining a final winner algorithm from comparisons but to discover and discuss the fundamental aspect of CE from viewpoint of chaotic optimization and optimization mechanism of CE algorithm with different chaotic systems behind the evaluation results. Some study subjects are analysed and discussed taking into account evaluation results and statistical tests. These topics are the relationship between distribution characteristic of chaotic system and optimization performance, algorithm ranking, interactive CE algorithm, disadvantages and improvement approaches of CE algorithm, a notation system of CE, and so forth. These research subjects present the originality and primary contribution of this paper.

Following this introductory section, an overview of the CE algorithm framework and chaotic systems used in this paper are reported in Section 2. We present several new CE algorithms in this paper, which demonstrates scalability of the chaotic algorithm framework. Interactive CE (ICE) algorithm framework is introduced in Section 2 as well. In Section 3, experimental evaluations of chaotic evolution are performed and some statistical tests are applied. We discuss our proposed CE algorithms and obtained evaluation results. In Section 4, we analyse our proposed new IEC algorithm. We discuss some issues based on ICE experimental simulation using a pseudo-IEC user, which is implemented by a Gaussian mixture model. The topic on algorithmic notation of CE is defined for its further development and study. Finally, we conclude the whole work, and some open topics, further opportunities, and future works are discussed in Section 5.

#### 2. Chaotic Evolution Algorithm Framework, Interactive Chaotic Evolution, and Chaotic System

##### 2.1. A Brief Review of Chaotic Evolution Algorithm

Deterministic system and stochastic system are two views in which we understand and describe the nature in philosophy and in science. They lead to two corresponding algorithm methodologies in optimization field, that is, deterministic and stochastic optimization algorithms. Evolutionary computation can be partially categorized into the later one, that is, stochastic optimization algorithm. However, since chaotic phenomenon and mechanism were found [3, 4], chaos and chaotic system are as well a tool to describe and study the nature besides deterministic and stochastic systems. In optimization field, there must be a set of chaotic optimization algorithms inspired and normalized by chaotic philosophy and methodology. This is the fundamental motivation that prompts us to discover and study new optimization algorithms in the viewpoint of chaotic optimization.

The concepts of “evolution” and “chaos” have more the same characteristics in common [6]. First, both words refer to the phenomena that need to be explained and to the theories that are supported to do the explaining [10]. Second, there must be an iteration in both evolution and chaos processes, so the phenomena of them can appear. Third, the concepts and theories of evolution and chaos influence and enhance all research works by introducing and fusing the fundamentals of their concepts and approaches.

Inspired from chaotic motion of nonlinear system that has an ergodicity property, a chaotic ergodicity based EC algorithm,* chaotic evolution*, was proposed and studied recently [6]. It simulates the chaotic motion in a search space for implementing optimization. Because chaotic motion has an ergodicity property, the proposed algorithm can guarantee its global convergence partially. The scalability of the algorithm is better than other EC algorithms by introducing different chaotic systems.

Suppose that an array on the left side of Figure 1 presents individuals, contour lines at the right side are a fitness landscape, and circles on the landscape are the individuals. CE algorithm for one search generation is described below. It is repeated until a satisfied solution is found or the search reaches to the desired generations.(1)Choose one individual as a target vector.(2)Obtain a chaotic parameter from a chaotic system (3)Make a mutant vector by (4)Generate a chaotic vector by crossing the target vector and the mutant vector (5)Compare the target vector and the chaotic vector and choose whichever a better one as offspring in the next generation.(6)Go to (1) and generate other offspring until all individuals are replaced with offspring in the next generation.