Evolution of Robustness in an Electronics Design

Thompson, Adrian; Layzell, P.

doi:10.1007/3-540-46406-9_22

Cited by 45 publications

(29 citation statements)

References 6 publications

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“…Using ideas inspired from embryology, the circuits can functionally self-organise, while redundant biodules enable damage to be overcome. Adrian Thompson has spent some years investigating how evolvable hardware can provide robust solutions, for example circuits that handle large variations in temperature and fabrication, by testing designs in different environments during evolution (Thompson and Layzell, 2000). More recently, Julian Miller has described experiments evolving developmental programs to create "French Flag" patterns (Miller and Banzhaf, 2003).…”

Section: Developmentmentioning

confidence: 99%

Controlling Robots with Fractal Gene Regulatory Networks

Bentley

2005

Recent Developments in Biologically Inspired Computing

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Abstract. Fractal proteins are a new evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns, that in turn can be used to solve problems. This chapter introduces the fractal development algorithm in detail and describes the use of fractal gene regulatory networks for learning a robot path through a series of obstacles. The results indicate the ability of this system to learn regularities in solutions and automatically create and use modules.

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Section: Developmentmentioning

confidence: 99%

Controlling Robots with Fractal Gene Regulatory Networks

Bentley

2005

Recent Developments in Biologically Inspired Computing

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“…Previous studies have indicated that temporally varying environments can affect several properties of evolved systems such as their structure (6), robustness (21), evolvability (22,23), and genotype-phenotype mapping (10,24). In particular, goals that change over time in a modular fashion (18), such that each new goal shares some of the subproblems with the previous goal, were found to spontaneously generate systems with modular structure (18).…”

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confidence: 99%

Varying environments can speed up evolution

Kashtan

Noor

Alon

2007

Proc. Natl. Acad. Sci. U.S.A.

265

272

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Simulations of biological evolution, in which computers are used to evolve systems toward a goal, often require many generations to achieve even simple goals. It is therefore of interest to look for generic ways, compatible with natural conditions, in which evolution in simulations can be speeded. Here, we study the impact of temporally varying goals on the speed of evolution, defined as the number of generations needed for an initially random population to achieve a given goal. Using computer simulations, we find that evolution toward goals that change over time can, in certain cases, dramatically speed up evolution compared with evolution toward a fixed goal. The highest speedup is found under modularly varying goals, in which goals change over time such that each new goal shares some of the subproblems with the previous goal. The speedup increases with the complexity of the goal: the harder the problem, the larger the speedup. Modularly varying goals seem to push populations away from local fitness maxima, and guide them toward evolvable and modular solutions. This study suggests that varying environments might significantly contribute to the speed of natural evolution. In addition, it suggests a way to accelerate optimization algorithms and improve evolutionary approaches in engineering.biological physics ͉ modularity ͉ optimization ͉ systems biology A central question is how evolution can explain the speed at which the present complexity of life arose (1-17). Current computer simulations of evolution are well known to have difficulty in scaling to high complexity. Such studies use computers to evolve artificial systems, which serve as an analogy to biological systems, toward a given goal (6, 9, 18). The simulations mimic natural evolution by incorporating replication, variation (e.g., mutation and recombination), and selection. Typically, a logarithmic slowdown in evolution is observed: longer and longer periods are required for successive improvements in fitness (6,9,18) [similar slowdown is observed in adaptation experiments on bacteria in constant environments (19,20)]. Simulations can take many thousands of generations to reach even relatively simple goals, such as Boolean functions of several variables (9, 18). Thus, to understand the speed of natural evolution, it is of interest to find generic ways, compatible with natural conditions, in which evolution in simulations can be speeded.To address this, we consider here the fact that the environment of organisms in nature changes over time. Previous studies have indicated that temporally varying environments can affect several properties of evolved systems such as their structure (6), robustness (21), evolvability (22, 23), and genotype-phenotype mapping (10, 24). In particular, goals that change over time in a modular fashion (18), such that each new goal shares some of the subproblems with the previous goal, were found to spontaneously generate systems with modular structure (18).Here, we study the effect of temporally varying environments on the speed...

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“…In the evolutionary robotics area, Lee et al [34] and Jakobi [23] evolved robust mobile robot controllers by training them in multiple trials of simulation, using genetic programming and a genetic algorithm, respectively. The active area of evolving robust systems is evolvable hardware [35][36][37]. Most of these approaches employ genetic algorithms or evolution strategies as the search procedures.…”

Section: Related Workmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

Zou

2018

Algorithms

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Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.

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Evolution of Robustness in an Electronics Design

Cited by 45 publications

References 6 publications

Controlling Robots with Fractal Gene Regulatory Networks

Controlling Robots with Fractal Gene Regulatory Networks

Varying environments can speed up evolution

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

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