What Software Evolution and Biological Evolution Don¿t Have in Common

Nehaniv, Chrystopher L.; Hewitt, Jill; Christianson, Bruce; Wernick, Paul

doi:10.1109/software-evolvability.2006.18

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…Although human minds are involved (instead of the blind watchmaker) and software systems are not biological entities (Nehaniv et al 2006), tinkering seems to be largely responsible for the shaping of these large-scale technologies. Moreover, common terms in software design include fault-tolerance, reliability, and extensibility, the last two closely related to the internal evolvability of the software system.…”

Section: Discussionmentioning

confidence: 99%

Convergent Evolutionary Paths in Biological and Technological Networks

2011

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Technology seems to follow a different type of evolutionary dynamic when compared with biological systems. As pointed out by Francois Jacob, evolution takes place by means of extensive tinkering and does not foresee the future. Engineers will typically have a well-defined purpose and are not-in principle-constrained by the available technological constraints. However, the truth is that technological change shares much more than we might suspect with the patterns and processes displayed by evolution. Using case studies from both protein maps and large-scale software networks, we show that several key traits, such as scale-free structure and modularity, are shared by both man-made and biological evolving systems. Surprisingly, we find convergent evolution in several key features of software systems, indicating that strong constraints are at work. Such constraints force engineers to extensively reuse already constructed parts, thus de facto tinkering with their designs in a way similar to the duplication-diversification mechanism driving genome growth. The evolution of these systems reveals that well-defined patterns are obtained "for free." Some

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Section: Discussionmentioning

confidence: 99%

Convergent Evolutionary Paths in Biological and Technological Networks

2011

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“…The persistence and spread of technological and cultural artifacts through time with variation is analogous to variation with heritability in that both are examples of what Darwin called “descent with modification” (Nehaniv et al, 2006). Biased (non-random) selection can be imposed on both types of processes and consequently both can undergo a form of evolution.…”

Section: The Implications Of Degeneracy In Evolution Technology Amentioning

confidence: 99%

Degeneracy allows for both apparent homogeneity and diversification in populations

Whitacre

Atamas

2012

Biosystems

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Trait diversity – the substrate for natural selection – is necessary for adaptation through selection, particularly in populations faced with environmental changes that diminish population fitness. In habitats that remain unchanged for many generations, stabilizing selection maximizes exploitation of resources by reducing trait diversity to a narrow optimal range. One might expect that such ostensibly homogeneous populations would have a reduced potential for heritable adaptive responses when faced with fitness-reducing environmental changes. However, field studies have documented populations that, even after long periods of evolutionary stasis, can still rapidly evolve in response to changed environmental conditions. We argue that degeneracy, the ability of diverse population elements to function similarly, can satisfy both the current need to maximize fitness and the future need for diversity. Degenerate ensembles appear functionally redundant in certain environmental contexts and functionally diverse in others. We propose that genetic variation not contributing to the observed range of phenotypes in a current population, also known as cryptic genetic variation (CGV), is a specific case of degeneracy. We argue that CGV, which gradually accumulates in static populations in stable environments, reveals hidden trait differences when environments change. By allowing CGV accumulation, static populations prepare themselves for future rapid adaptations to environmental novelty. A greater appreciation of degeneracy’s role in resolving the inherent tension between current stabilizing selection and future directional selection has implications in conservation biology and may be applied in social and technological systems to maximize current performance while strengthening the potential for future changes.

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“…al. [13] evolutionary concepts have to be carefully transferred into the realm of software engineering. The same is true for our work with regard to service fitness.…”

Section: Related Workmentioning

confidence: 99%

Calculating Service Fitness in Service Networks

Treiber

Andrikopoulos

Dustdar

2010

Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops

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Abstract. Inspired by the biological perspective of service ecosystems, we propose to define the fitness of services in service networks. In our work, we show how to calculate the service fitness from the provider perspective using locally available information as a reflection of the position of the service in the service network. For that purpose we define a fitness corridor with upper and lower bounds that confine the service fitness area. After establishing a fitness corridor, we show how to calibrate the fitness calculation parameters to better reflect the service market and how to use the calculated fitness trends for making decisions about the provisioning of a service.

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What Software Evolution and Biological Evolution Don¿t Have in Common

Cited by 17 publications

References 28 publications

Convergent Evolutionary Paths in Biological and Technological Networks

Convergent Evolutionary Paths in Biological and Technological Networks

Degeneracy allows for both apparent homogeneity and diversification in populations

Calculating Service Fitness in Service Networks

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