Editing Natural selection (section)

===Information and systems theory===
In 1922, [[Alfred J. Lotka]] proposed that natural selection might be understood as a physical principle that could be described in terms of the use of energy by a system,<ref>{{cite journal |last=Lotka |first=Alfred J. |author-link=Alfred J. Lotka |date=June 1922 |title=Contribution to the energetics of evolution |journal=PNAS |volume=8 |issue=6 |pages=147–151 |doi=10.1073/pnas.8.6.147 |pmc=1085052 |pmid=16576642|bibcode=1922PNAS....8..147L |doi-access=free }}</ref><ref>{{cite journal |last=Lotka |first=Alfred J. |date=June 1922 |title=Natural selection as a physical principle |journal=PNAS |volume=8 |issue=6 |pages=151–154 |doi=10.1073/pnas.8.6.151 |pmc=1085053 |pmid=16576643|bibcode=1922PNAS....8..151L |doi-access=free }}</ref> a concept later developed by [[Howard T. Odum]] as the [[maximum power principle]] in [[thermodynamics]], whereby evolutionary systems with selective advantage maximise the rate of useful energy transformation.<ref>{{cite book |author=Odum, H. T. |author-link=Howard T. Odum |date=1995 |title=Self-Organization and Maximum Empower |editor=Hall, C. A. S. |publisher=Colorado University Press}}</ref>

The principles of natural selection have inspired a variety of computational techniques, such as "soft" [[artificial life]], that simulate selective processes and can be highly efficient in 'adapting' entities to an environment defined by a specified [[fitness function]].<ref>{{harvnb|Kauffman|1993}}</ref> For example, a class of heuristic [[Mathematical optimization|optimisation]] algorithms known as [[genetic algorithm]]s, pioneered by [[John Henry Holland]] in the 1970s and expanded upon by [[David E. Goldberg]],<ref>{{harvnb|Goldberg|1989}}</ref> identify optimal solutions by simulated reproduction and mutation of a population of solutions defined by an initial [[probability distribution]].<ref>{{harvnb|Mitchell|1996}}</ref> Such algorithms are particularly useful when applied to problems whose [[energy landscape]] is very rough or has many local minima.<ref>{{cite web |title=Genetic Algorithms |url=http://www.pharmacologicalsciences.us/genetic-algorithms/scoring-functions.html |website=Pharmacological Sciences |date=7 November 2016 |access-date=7 November 2016}}</ref>