Towards the automatic design of more efficient digital circuits

Vassilev, V.; Job, Dominic; Miller, Julian F.

doi:10.1109/eh.2000.869353

Cited by 80 publications

(47 citation statements)

References 13 publications

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“…We applied a standard evolutionary algorithm (13)(14)(15) to seek a circuit that maximizes fitness and thus achieves the goal (20,35). We start with a population of random genomes that represent randomly wired circuits.…”

Section: Resultsmentioning

confidence: 99%

“…A well known feature of these computational models of biological evolution is that the evolved systems are usually intricately wired and nonmodular. The nonmodular solutions are often more highly optimized than their human-engineered counterparts (16,17,20).The fundamental reason for the lack of modularity in these evolved networks is that modular structures are usually less optimal than nonmodular ones. Typically, there are many possible connections that break modularity and increase fitness.…”

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confidence: 99%

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confidence: 99%

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Spontaneous evolution of modularity and network motifs

Kashtan

Alon²

2005

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

779

803

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Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such ''modularly varying goals'' lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems. B iological and engineered systems share general design features: they display modularity, defined as the separability of the design into units that perform independently, at least to a first approximation (1-3, 5). † Furthermore, they show reuse of certain circuit patterns, termed network motifs (6-11), in many different parts of the system. These features allow construction of extremely complex systems by using simple building blocks (12).These features of biological networks are not captured by most current models of biological evolution. For example, many models of biological evolution use computers to evolve networks to attain a defined goal. In these simulations, networks in a population are varied by means of mutations, crossover, and duplication (13-15). Networks that perform better are selected in the next generation. A well known feature of these computational models of biological evolution is that the evolved systems are usually intricately wired and nonmodular. The nonmodular solutions are often more highly optimized than their human-engineered counterparts (16,17,20).The fundamental reason for the lack of modularity in these evolved networks is that modular structures are usually less optimal than nonmodular ones. Typically, there are many possible connections that break modularity and increase fitness. Thus, even an initially modular solution rapidly evolves into one of many possible nonmodular solutions.Lack of modularity is one of the reasons that computational evolution can currently generate designs for simple tasks, but has difficulty in scaling up to higher complexity. In the field of evolutionary design of engineered systems, approaches have been dev...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Spontaneous evolution of modularity and network motifs

Kashtan

Alon²

2005

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

779

803

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“…3-bit multipliers were also successfully evolved, however this required a much higher computational effort. The practical limit to the size of directly evolvable multiplier circuits seems, from the experiments in [43], to be 4 bits.…”

Section: Scalabilitymentioning

confidence: 99%

A Flexible On-Chip Evolution System Implemented on a Xilinx Virtex-II Pro Device

Glette

Tørresen

2005

Evolvable Systems: From Biology to Hardware

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“…genetic algorithms) to create electronic circuits. This approach showed that novel or more efficient circuits than those obtained with traditional techniques can be found [9,79,86]. EHW is believed to have a lot of potential [9], for instance in adaptive hardware [39], or in fault-tolerant hardware [43], and it gains support in the industrial community [35].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary morphogenesis for multi-cellular systems

Roggen

Federici

Floreano

2006

Genet Program Evolvable Mach

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With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for

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Towards the automatic design of more efficient digital circuits

Cited by 80 publications

References 13 publications

Spontaneous evolution of modularity and network motifs

Spontaneous evolution of modularity and network motifs

A Flexible On-Chip Evolution System Implemented on a Xilinx Virtex-II Pro Device

Evolutionary morphogenesis for multi-cellular systems

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