Novelty Search makes Evolvability Inevitable

被引:10
作者
Doncieux, Stephane [1 ]
Paolo, Giuseppe [1 ,2 ]
Laflaquiere, Alban [2 ]
Coninx, Alexandre [1 ]
机构
[1] Sorbonne Univ, CNRS, Inst Syst Intelligents & Robot, ISIR, F-75005 Paris, France
[2] SoftBank Robot Europe, AI Lab, Paris, France
来源
GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2020年
关键词
Novelty search; evolutionary robotics; evolvability; behavior space; EVOLUTIONARY ROBOTICS; OPTIMIZATION; DIVERSITY;
D O I
10.1145/3377930.3389840
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straight-forward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reachable behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability.
引用
收藏
页码:85 / 93
页数:9
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