Evolution in nature has developed complex organisms with varied capabilities that have successfully adapted to changing environmental conditions. Evolutionary computation (EC) is the field of science that aims to develop engineering and problem-solving tools by modeling natural evolution. Existing EC algorithms are mostly built around the principle of selection and survival of the fittest in nature. This has limited their adaptive potential and placed a large emphasis on the fitness function used to model the problem objectives.
In this project, a novel evolutionary framework will be developed that better models natural evolution by incorporating viability-based events. The core intuition is a shift of emphasis from an evolutionary process purely driven by selection of the fittest to one that is founded on the notion of viability of the evolving individuals. The immediate benefits of the proposed framework include the elimination of a single fitness function, and the ability to seamlessly incorporate continuously changing environments and/or task constraints. In order to realize its full potential, the proposed framework will be coupled with the previously developed Analog Genetic Encoding (AGE). The proposed viability evolution framework will then be validated in two scenarios, namely, synthesis of robot control circuits under changing environmental conditions or task constraints, and incremental reverse engineering of large gene regulatory networks.
This project is expected to contribute significantly to the fields of multi-objective optimization and open-ended evolution. From a multi-objective optimization perspective, the proposed framework introduces a radically new method for specifying the multiple objectives using viability constraints. From an incremental evolution perspective, the proposed framework will allow the gradual addition and elimination of viability criteria without the need for redefining the fitness function. The formalization and implementation of dynamic viability will offer a novel, principled way for performing incremental evolution. The two validation scenarios are also expected to have significant impact in their respective fields. The proposed method will offer a novel way to incrementally evolve robotic systems that can adapt to changing environmental conditions, and/or task constraints. Our method may allow for the discovery of the interactions among gene networks of unprecedented complexity and may possibly link network structure with functional effects. Consequently, it could lead to significant advancements that allow the testing of gene pharmacology and personalized medicine.
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