ZCS: A Zeroth Level Classifier System

نویسنده

  • Stewart W. Wilson
چکیده

A basic classifier system, ZCS, is presented which keeps much of Holland’s original framework but simplifies it to increase understandability and performance. ZCS’s relation to Q-learning is brought out, and their performances compared in environments of two difficulty levels. Extensions to ZCS are proposed for temporary memory, better action selection, more efficient use of the genetic algorithm, and more general classifier representation.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones. There are some undiscounted reinforcement learning methods available, such as ...

متن کامل

An Investigation into Island Model Rule Migration for a Number of Mobile Autonomous Learning Classifier System Agents

This work presents an investigation into combining the Island Model Genetic Algorithm and Learning Classifier System paradigms into a Multi-Agent approach to reinforcement learning. A multi-agent system is defined in which a number of interacting Zeroth Level Learning Classifier System agents inhabit a two-dimensional virtual test environment and are expected to perform a food-finding task. On ...

متن کامل

An experimental evaluation of ZCS-DM for the prediction of urban air quality

Understanding and forecasting urban Air Quality (AQ) is not only a multifaceted and computationally challenging problem for machine learning algorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services poses the need for robust, efficient and, more importantly, understandable forecasting m...

متن کامل

Adaptive Framework for Network Intrusion Detection by Using Genetic-Based Machine Learning Algorithm

Summery Computer networks have expanded significantly in use and in numbers. This expansion makes them target to different attacks. Intrusion Detection System (IDS) is used to identify unknown or new type of attacks or in dynamic environments as mobile networks. As a result, it is necessary to find a ways to implement and operate IDSs. Among different techniques, Genetic-based machine learning ...

متن کامل

Lookahead And Latent Learning In ZCS

Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Evolutionary Computation

دوره 2  شماره 

صفحات  -

تاریخ انتشار 1994