This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods
Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design...
Abstract:- Evolvable Hardware is a hardware which modifies its own structure in order to adapt to th...
This paper discusses the use of evolutionary algorithms to design digital circuits. It is shown that...
This paper disscusses two studies of using evolutionary algorithms in physical design for FPGAs. The...
This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct ...
In this paper, a hardware-based path planning architecture for unmanned aerial vehicle (UAV) adaptat...
The field of FPGA design is ever-growing due to costs being lower than that of ASICs, as well as the...
This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to sy...
© 2014 Technical University of Munich (TUM).Parallel genetic algorithms (pGAs) are a variant of gene...
Abstract—One very promising approach for solving complex optimizing and search problems is the Genet...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
The book is composed of two parts. The first part introduces the concepts of the design of digital s...
There has recently been much research interest in the concept of evolvable hardware —partly due to t...
Genetic algorithm (GA) is a directed random search technique working on a population of solutions a...
Summarization: This paper presents the implementation of a Genetic Algorithm on a XUPV2P platform wi...
Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design...
Abstract:- Evolvable Hardware is a hardware which modifies its own structure in order to adapt to th...
This paper discusses the use of evolutionary algorithms to design digital circuits. It is shown that...
This paper disscusses two studies of using evolutionary algorithms in physical design for FPGAs. The...
This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct ...
In this paper, a hardware-based path planning architecture for unmanned aerial vehicle (UAV) adaptat...
The field of FPGA design is ever-growing due to costs being lower than that of ASICs, as well as the...
This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to sy...
© 2014 Technical University of Munich (TUM).Parallel genetic algorithms (pGAs) are a variant of gene...
Abstract—One very promising approach for solving complex optimizing and search problems is the Genet...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
The book is composed of two parts. The first part introduces the concepts of the design of digital s...
There has recently been much research interest in the concept of evolvable hardware —partly due to t...
Genetic algorithm (GA) is a directed random search technique working on a population of solutions a...
Summarization: This paper presents the implementation of a Genetic Algorithm on a XUPV2P platform wi...
Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design...
Abstract:- Evolvable Hardware is a hardware which modifies its own structure in order to adapt to th...
This paper discusses the use of evolutionary algorithms to design digital circuits. It is shown that...