Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Defining Urban Complex Problems with Fuzzy Analysis: The Case of Söke Settlement in Turkey

This article aims to follow the application of fuzzy approach in the analysis of urban complex problems; classifying urban problems according to different criteria. It proposes a methodology to combine different dimensions of quality of life, with the economic (income, employment), social (education) physical (health and infrastructure) indicators into Quality of Life Index (QLI) by applying Totally Fuzzy Analysis (TFA). The objective of the present work is to identify, based on survey data of Söke settlement in Turkey, to define the sub zones according to life quality indicators. The sample for the survey is designed to provide representative samples of private households in Söke. A stratified random sample is selected such that every sampling unit in the population has an equal probability of being selected for the sample. From the population of 14582 housing units in Söke, a sample size of 366 was chosen. As a result, 366 households were interviewed without missing. The indicators that have been used for the fuzzy model consist of three main blocks. The first one in the indicators that describe development of socio-economic system is the economic indicators such as urban poverty (income and expenditures), property ownership, employment and attributes of the labor force. The second one is physical indicators that consist of availability of residential services, housing density and the quality of housing units. The third one is the social indicators which can be described as household profile, cultural expenditures and life patterns. The goal is achieved by applying a new and straightforward method of GIS and fuzzy logic. This methodology was applied in the study area and the results presented in the form of tables and maps. The results revealed that there are spatial, social and economic disparities in some parts of the area. The findings indicate that the fuzzy technics are powerful analytic tools for helping planners define urban complex problems and to see relations between social, economic and physical factors.