Evolvability Is Inevitable: Increasing Evolvability without the Pressure to Adapt
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{"title"=>"Evolvability Is Inevitable: Increasing Evolvability without the Pressure to Adapt", "type"=>"journal", "authors"=>[{"first_name"=>"Joel", "last_name"=>"Lehman", "scopus_author_id"=>"36439865800"}, {"first_name"=>"Kenneth O.", "last_name"=>"Stanley", "scopus_author_id"=>"7102875151"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"84876569713", "arxiv"=>"arXiv:1302.1143v1", "doi"=>"10.1371/journal.pone.0062186", "scopus"=>"2-s2.0-84876569713", "pui"=>"368794694", "isbn"=>"1932-6203", "pmid"=>"23637999"}, "id"=>"8f262b6c-3955-3c95-8a0f-3df309e63c2b", "abstract"=>"Why evolvability appears to have increased over evolutionary time is an important unresolved biological question. Unlike most candidate explanations, this paper proposes that increasing evolvability can result without any pressure to adapt. The insight is that if evolvability is heritable, then an unbiased drifting process across genotypes can still create a distribution of phenotypes biased towards evolvability, because evolvable organisms diffuse more quickly through the space of possible phenotypes. Furthermore, because phenotypic divergence often correlates with founding niches, niche founders may on average be more evolvable, which through population growth provides a genotypic bias towards evolvability. Interestingly, the combination of these two mechanisms can lead to increasing evolvability without any pressure to out-compete other organisms, as demonstrated through experiments with a series of simulated models. Thus rather than from pressure to adapt, evolvability may inevitably result from any drift through genotypic space combined with evolution's passive tendency to accumulate niches.", "link"=>"http://www.mendeley.com/research/evolvability-inevitable-increasing-evolvability-without-pressure-adapt", "reader_count"=>123, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>11, "Researcher"=>27, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>3, "Other"=>9, "Student > Master"=>20, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>11, "Researcher"=>27, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>3, "Other"=>9, "Student > Master"=>20, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>1, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>7, "Agricultural and Biological Sciences"=>59, "Medicine and Dentistry"=>2, "Philosophy"=>3, "Neuroscience"=>1, "Physics and Astronomy"=>2, "Psychology"=>3, "Social Sciences"=>3, "Computer Science"=>35}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>59}, "Computer Science"=>{"Computer Science"=>35}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>2}, "Philosophy"=>{"Philosophy"=>3}}, "reader_count_by_country"=>{"Hungary"=>1, "United States"=>4, "Japan"=>1, "United Kingdom"=>3, "Portugal"=>1, "Canada"=>1, "Netherlands"=>2, "Sweden"=>1, "Norway"=>1, "Luxembourg"=>1, "Brazil"=>2, "Israel"=>1, "France"=>1, "Germany"=>1}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1034725"], "description"=>"<p>The simulated mobile robot is shown that is used in the experiments with evolved machines. Rangefinder sensors allow the robot to perceive obstacles, and the motors controlling its wheels enable the robot to traverse its environment.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations"], "article_id"=>690541, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g010", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Wheeled_robot_/690541", "title"=>"Wheeled robot.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034707"], "description"=>"<p>The evolvability of organisms at the end of a simulation is shown as a function of distance from the initial starting niche (averaged over 50 independent runs). The main result is that there is a significant correlation between increasing distance from the initial niche and increasing evolvability in this model. The plotted line indicates the line of best fit by linear regression.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "niche", "passive"], "article_id"=>690523, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_vs_distance_from_the_initial_niche_for_the_abstract_passive_drift_model_/690523", "title"=>"Evolvability vs. distance from the initial niche for the abstract passive drift model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034721"], "description"=>"<p>The evolvability of organisms is shown averaged over 50 independent simulations that lasted 250 generations each. It is important to note that because the population size in the limited-niche model is much smaller, <i>one</i> generation of the passive model encompasses more individuals (2,000,000) than is considered over <i>all</i> generations in the niched model ( on average). In other words, the limited-niche model is more directedly and more efficiently biased towards evolvability. In particular, the difference in evolvability between the limited-niche model and the passive model is significant for all comparisons after the 50th generation (; Student's t-test).</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "evolvability", "evolved", "robots"], "article_id"=>690537, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g008", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_True_increasing_evolvability_in_the_evolved_robots_model_with_limited_capacity_niches_/690537", "title"=>"True increasing evolvability in the evolved robots model with limited capacity niches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034733"], "description"=>"<p>The initial topology of the ANN in the practical ER model is shown. Topologies change during evolution from structural mutations that add new nodes and connections. In addition, unlike in the restricted ANN space, connection weights can vary continuously, i.e. weight mutations perturb connections with values drawn from the uniform distribution, and weights are capped between and 3.0.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations"], "article_id"=>690549, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g013", "stats"=>{"downloads"=>3, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_NEAT_ANN_/690549", "title"=>"NEAT ANN.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:09:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034718"], "description"=>"<p>The average evolvability of organisms in the final population is shown as a function of distance from the initial starting niche averaged over 50 independent simulations. The main result is that there is a significant correlation between increasing distance from the initial niche and increasing evolvability. The plotted line indicates the line of best fit by linear regression.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "niche", "limited-capacity"], "article_id"=>690534, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g007", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_vs_distance_from_the_initial_niche_for_the_abstract_model_with_limited_capacity_niches_/690534", "title"=>"Evolvability vs. distance from the initial niche for the abstract model with limited-capacity niches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034715"], "description"=>"<p>The average evolvability of organisms in each niche at the end of a simulation is shown. The lighter the color, the more evolvable individuals are within that niche. The overall result is that, as in the first model, evolvability increases with increasing distance from the starting niche in the center.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations"], "article_id"=>690531, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g006", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_heat_map_for_the_abstract_model_with_limited_capacity_niches_/690531", "title"=>"Evolvability heat map for the abstract model with limited capacity niches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034700"], "description"=>"<p>The average evolvability of organisms in each niche at the end of a simulation is shown averaged over 50 independent runs. The lighter the color, the more evolvable individuals are within that niche. The overall result is that evolvability increases with increasing distance from the starting niche in the center.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "passive"], "article_id"=>690516, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_heat_map_for_the_abstract_passive_drift_model_/690516", "title"=>"Evolvability heat map for the abstract passive drift model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034728"], "description"=>"<p>A top-down view of the maze is shown that robots navigate in the experiments with evolved machines. The circle indicates where a robot begins its trial in the maze, which lasts for 400 simulated timesteps.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations"], "article_id"=>690544, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g011", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maze_environment_/690544", "title"=>"Maze environment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:09:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034723"], "description"=>"<p>The average evolvability of organisms in the population over evolutionary time is shown, which is itself averaged over 50 independent simulations that lasted 50,000 evaluations each. Note that evolvability of an organism is measured as the average number of different behaviors generated through 300 random mutations. The main result is that niching based on behavior significantly increases evolvability over a control that randomly assigns niches independently of an organism's behavior ( for all comparisons after 2,500 evaluations; Student's t-test).</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "er"], "article_id"=>690539, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g009", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_in_the_practical_ER_model_with_limited_capacity_niches_/690539", "title"=>"Evolvability in the practical ER model with limited capacity niches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034708"], "description"=>"<p>The evolvability of evolved robots subject to passive drift is shown averaged (in different ways) over 50 independent runs that lasted 250 generations each. If evolvability is averaged within each niche and then over all niches, it appears to increase. If instead evolvability is averaged over all organisms, there is no significant increase.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "evolved", "robots", "passive"], "article_id"=>690524, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g004", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_of_evolved_robots_with_the_passive_drift_model_/690524", "title"=>"Evolvability of evolved robots with the passive drift model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034731"], "description"=>"<p>The figure illustrates the fully-connected recurrent ANN with 18 possible connections that serves as a space of possible controllers for robots embedded in a maze navigation environment. Each connection can either be excitatory (a weight of 1.0), inhibitory () or neutral (0.0). The activation function in the ANN is a steepened sigmoid function <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062186#pone.0062186-Stanley1\" target=\"_blank\">[18]</a>. The ANN has three rangefinder sensor inputs, two hidden neurons, and two motor outputs.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations"], "article_id"=>690547, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g012", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fixed_topology_ANN_/690547", "title"=>"Fixed-topology ANN.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:09:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034698"], "description"=>"<p>How the evolvability of organisms changes over generations is shown averaged (in different ways) over 50 independent simulations that last 3,000 generations each. If evolvability is averaged <i>within</i> each niche and then <i>over</i> all niches, then evolvability <i>appears</i> to increase. However, if instead evolvability is simply averaged over all organisms in the population, there is no significant overall increase in evolvability over time.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "passive"], "article_id"=>690514, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolvability_in_the_abstract_passive_drift_model_/690514", "title"=>"Evolvability in the abstract passive drift model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034711"], "description"=>"<p>How the average evolvability of organisms in the population changes over time is shown (averaged over 50 independent simulations that lasted 1000 generations each). Note that the line shown for the passive model (reproduced from figure 0) represents only the <i>appearance</i> of increasing evolvability in that model when evolvability is averaged over niches.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "Evolutionary processes", "adaptation", "emergence", "Evolutionary selection", "Forms of evolution", "Divergent evolution", "neuroscience", "neural networks", "Computerized simulations", "evolvability"], "article_id"=>690527, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Joel Lehman", "Kenneth O. Stanley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062186.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_True_increasing_evolvability_in_the_abstract_model_with_limited_capacity_niches_/690527", "title"=>"True increasing evolvability in the abstract model with limited capacity niches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:08:47"}

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