Distributed Embodied Evolution over Networks (2003.12848v4)

Abstract: In several network problems the optimum behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable. In this work, we use a distributed Embodied Evolution approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our results on several test scenarios show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to conduct the optimization process more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behavior parameters within each agent's neighborhood.

• The paper introduces a novel method where local parameter exchange enhances autonomous optimization among network agents.
• It employs a population-less evolutionary algorithm validated through experiments on IoT, sensor arrays, and human activity detection.
• The study shows that random parameter crossover outperforms best-neighbor selection, paving the way for adaptive, self-tuning distributed systems.

Distributed Embodied Evolution over Networks

The paper presented introduces a nuanced approach to optimizing the behavior of spatially distributed, locally interacting agents through a distributed Embodied Evolution (EE) method. This research addresses scenarios where the optimal behavior of network agents is unknown prior to deployment and where adaptation to environmental changes is requisite. The paper falls under the umbrella of bio-inspired techniques, employing distributed Evolutionary Algorithms (EAs) to realize local, autonomous optimization in networked environments.

Core Contributions

A primary contribution of this work is the demonstration of EE in the context of networks, with particular emphasis on Internet of Things (IoT) systems. Unlike traditional EE applications focused on robotics, this paper extends the paradigm to static networks such as sensor arrays or communication nodes, marking a novel field of application.

Key contributions include:

1. Introduction of Local Parameter Exchange: The paper explores various local parameter exchange strategies—ranging from copying parameters from neighbors to performing crossover—that facilitate local optimization despite differences in optimal behavior among adjacent agents.
2. Comprehensive Experimentation: The paper devises three test problems, including a real-world IoT application, and assesses the EE approach in different network configurations, providing comprehensive statistical analysis of the results.

Methodological Approach

The paper outlines a method wherein each networked agent autonomously evolves its behavior using a population-less EA. This evolution is guided by local interactions, where agents can exchange behavior parameters with neighbors, akin to crossover in traditional EAs. This local exchange is tested under various parameter settings, and its performance evaluated across distinct scenarios, notably:

• Imitation Problem: Agents learn to replicate sequences of images (adapted from the MNIST dataset) over a grid-like network.
• Illumination Problem: Optimization of illumination levels simulates dynamic environmental conditions across time and space.
• IoT Human Presence and Activity Detection: Application to a distributed model utilizing real-world data to detect human presence and activity using neural networks.

Results and Implications

The paper finds that local information exchange, even with significant parameter differences between neighbors, enhances optimization efficiency. Specifically, crossover operations where behavior parameters are exchanged randomly, rather than selectively from the best neighbor, yield superior results. This insight contradicts intuitive assumptions that the best-performing neighbor's parameters might lead to optimal convergence.

Implications of this research extend to the domain of autonomous, distributed system management—highlighting EE's potential in self-optimizing network systems. The findings suggest future exploration in adapting these approaches to networks with dynamic topologies and imperfect communication, which mimic more realistic scenarios.

Future Directions

Building on these insights, the paper hints at potential future research avenues:

• Adaptation of self-tuning parameter methods to cater to dynamically changing environments.
• Exploration of EE in systems where local fitness functions are non-comparable.
• Hybridization with gradient-based optimization for a more robust convergent behavior in diverse network topologies.

Overall, this research paves the way for incorporating EE in distributed systems where local adaptation due to environmental discrepancies is paramount, offering foundational results that could be expanded through ongoing research into dynamic networks and AI-driven optimization.