- Tushar Semwal
- Divya D Kulkarni
- 'Shivashankar B. Nair'
title: 'On an Immuno-inspired Distributed, Embodied Action-Evolution cum Selection Algorithm '
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&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\_id&gt;10010520.10010553.10010554.10010556.10011814&lt;/concept\_id&gt; &lt;concept\_desc&gt;Computer systems organization Evolutionary robotics&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;

Introduction
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While in the biological realm, survival may be the only intrinsic motivation behind *evolution*, in Evolutionary Robotics (ER) [@nolfi1998evolutionary], the accomplishment of a set of tasks forms an additional component. These two motives together are deemed necessary in both single and multi-robot scenarios. Due to their inherent resistance to noise and smooth input to output mapping, several researchers have used Artificial Neural Networks (ANN) [@floreano3automatic] to evolve controllers for such evolutionary robots [@nelson2009fitness].

Traditional ER approaches incorporate a series of an evolution-guided search for a single *monolithic* robot controller which can aid a robot to achieve the desired tasks. The search for a single optimal robotic controller is performed using several iterations that involve multiple runs of the associated algorithm on centralized (and sequential) simulation environments. The controller is then embedded in real robots. Single controllers have been evolved to implement different tasks such as obstacle avoidance, gait learning and search tasks [@nelson2009fitness]. However, bootstrapping the evolutionary process becomes difficult especially when the tasks 