We describe a high-speed pattern recognition system using Evolvable Hardware (EHW), which can change its own hardware structure by genetic learning in order to adapt best to the environment. The purpose of the system is to show that EHW can work as a recognition device with such robustness for the noise as seen in the recognition systems based on neural networks. The advantage of EHW compared w...

Evolvable Hardware (EHW) has been proposed as a new method for designing systems for complex real world applications. One of the problems has been that only small and simple systems have been evolvable. This paper highlights some of the reasons that can explain why EHW has not yet been widely applied. Further, to make EHW more applicable, an increased complexity scheme is proposed, where a syst...

Evolvable Hardware (EHW) is a new scheme inspired by natural evolution, for designing hardware systems. By exploring a large design search space, EHW may find solutions for a task, unsolvable, or more optimal than those found using traditional design methods. The paper introduces this new approach and outlines how it can be applied for hardware design of real-world applications. It continues by...

The RISA Architecture is a novel reconfigurable hardware platform containing both hardware and software reconfigurable elements. This paper describes the architecture and the features that make it suitable for implementing biologically inspired systems such as the evolution of digital circuits. Some of the architecture’s capabilities are demonstrated with the results of evolving a simple combin...

To solve the genome complexity issue and enable evolution of large complex circuits, the need to move away from a one-to-one genotype/phenotype mapping is becoming generally accepted. This involves development of new forms of representation with features such as growth. Shrinking the size of the genotype in effect moves complexity from the genotype representation to the genotype/phenotype mappi...

This chapter introduces the idea of " Evolvable Hardware, " which applies evolutionary algorithms to the generation of programmable hardware as a means of achieving Artificial Intelligence. Cellular Automata-based Neural Networks are evolved in different modules, which form the components of artificial brains. Results from past models and plans for future work are presented.

This paper considers the ability of an evolved circuit to acquire fault tolerance by including fault conditions within the fitness measure of the evolutionary process. A simple oscillator circuit, implemented on a Xilinx FPGA, showed a 12 fold increase in fault-tolerance when compared to a control oscillator using realistic faults.

Evolvable Hardware (EHW) is a hardware which modifies its own structure in order to adapt to the environment in which it is embedded. This reconfigurable hardware is implemented on a programmable circuit, whose architecture can be altered by downloading a binary bit string. These bits are adaptively acquired by evolutionary algorithms. In this paper we have used an evolutionary algorithm to des...

This paper describes an evolvable hardware (EHW) system for generalized neural network learning. We have developed an ASIC VLSI chip, which is a building block to configure a scalable neural network hardware system. In our system, both the topology and the hidden layer node functions of a neural network mapped on the chips are dynamically changed using a genetic algorithm. Thus, the most desira...