Rapid Extinctions Boosting Evolution, Suggest Computer Scientists!
In a groundbreaking study, computer scientists Risto Miikkulainen and Joel Lehman from the University of Texas in Austin have demonstrated how simulated mass extinctions can accelerate the evolution of artificial neural networks in robots, leading to the emergence of new traits and abilities.
The researchers attached artificial neural networks to simulated robot legs and programmed the robots to walk smoothly and steadily, with random mutations occurring throughout the process. The study, published in PLOS One, shows that mass extinctions can affect the evolution of these computer-based neural networks.
The concept behind this study is based on the idea of "evolutionary landscapes", as discussed in the book "Hill Climbing: The Path to Excellence Cannot Be Planned" written by Lehman, Miikkulainen, and their former student Kenneth Stanley from UT Austin. This concept suggests that even destruction can serve as a catalyst for evolutionary creativity.
In the study, mass extinctions were simulated as strong selective pressures that removed less-adapted individuals en masse. The surviving robots and their descendants faced less competition and more resources, driving rapid diversification and innovation. This cycle encouraged evolution towards new behaviors and capabilities, accelerating the pace at which complex traits emerged.
This finding aligns with findings in biological and ecological evolutionary modeling, where sudden reductions in diversity (mass extinctions) followed by species diversification promote continuous evolution and novelty. The study's findings provide a great example of how evolution can create amazing things through indirect and winding paths.
The potential applications of this research are far-reaching. For instance, the findings can have practical applications in the development of robots that can overcome obstacles more easily, such as those working in earthquake rubble or on Mars, in minefields. The study also suggests that the principles discovered can be applied to the creation of human-like game characters.
Some evolutionary biologists hypothesize that mass extinctions can accelerate the evolution of the most adaptable lineages. The study's findings support this idea, providing evidence that mass extinctions can indeed encourage the emergence of new traits and abilities in surviving lineages, specifically in robots.
In conclusion, simulated mass extinctions accelerate evolution by clearing out dominant existing forms, triggering rapid adaptation and innovation in robot populations through intensified selective pressure and expansion into available niches. This process fosters the rapid emergence of new traits and abilities in evolving robots.
On a par with biological and ecological evolutionary modeling, the acceleration of artificial neural network evolution in robots can occur through simulated mass extinctions, leading to the emergence of novel traits and abilities. This concept, backed by the principles of evolutionary landscapes, indicates that even destructive events like mass extinctions can serve as catalysts for evolutionary creativity. As demonstrated by the study's results, the successful application of these insights could potentially enhance the capabilities of robots, enabling them to navigate challenging terrains, such as earthquake-ridden regions or Mars, or even in creating more advanced human-like game characters.
