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Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Trianni V, López-Ibáñez M - PLoS ONE (2015)

Bottom Line: A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled).This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics.In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.

ABSTRACT
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

No MeSH data available.


Strictly Collaborative Task.Fitness of the best individual across generation for all evolutionary runs, for n = 2 (top) and n = 3 (bottom). Note that the maximum fitness achievable with n = 3 is lower than with n = 2, due to the increased complexity of the task.
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pone.0136406.g009: Strictly Collaborative Task.Fitness of the best individual across generation for all evolutionary runs, for n = 2 (top) and n = 3 (bottom). Note that the maximum fitness achievable with n = 3 is lower than with n = 2, due to the increased complexity of the task.

Mentions: As mentioned above, we study two different task complexities by setting the collaboration level to n = 2 and n = 3. We run single-objective evolution using 𝓢c,n as fitness function. For each evolutionary run, we first observe the trend of the fitness of the best individual in the population during the evolutionary optimisation, as shown in Fig 9. We notice that the bootstrap problem only mildly affects evolution with n = 2. Several evolutionary runs do show a flat fitness surface for many generations, which indicates that the whole population scored a fitness. However, by random drift, some solutions with non- fitness appeared and optimisation rapidly started. This indicates that the bootstrap problem exists, but is not too severe. On the contrary, when n = 3 the situation is worse, as can be seen in the bottom part of Fig 9. Most of the evolutionary runs suffered of the bootstrap problem, and present a very flat fitness surface. Only a minority of runs were able to discover a suitable strategy that was optimised through the generations. This also indicates that there exist possible solutions to the proposed problem, but they are not easily obtained by a SOO approach.


Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Trianni V, López-Ibáñez M - PLoS ONE (2015)

Strictly Collaborative Task.Fitness of the best individual across generation for all evolutionary runs, for n = 2 (top) and n = 3 (bottom). Note that the maximum fitness achievable with n = 3 is lower than with n = 2, due to the increased complexity of the task.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4546428&req=5

pone.0136406.g009: Strictly Collaborative Task.Fitness of the best individual across generation for all evolutionary runs, for n = 2 (top) and n = 3 (bottom). Note that the maximum fitness achievable with n = 3 is lower than with n = 2, due to the increased complexity of the task.
Mentions: As mentioned above, we study two different task complexities by setting the collaboration level to n = 2 and n = 3. We run single-objective evolution using 𝓢c,n as fitness function. For each evolutionary run, we first observe the trend of the fitness of the best individual in the population during the evolutionary optimisation, as shown in Fig 9. We notice that the bootstrap problem only mildly affects evolution with n = 2. Several evolutionary runs do show a flat fitness surface for many generations, which indicates that the whole population scored a fitness. However, by random drift, some solutions with non- fitness appeared and optimisation rapidly started. This indicates that the bootstrap problem exists, but is not too severe. On the contrary, when n = 3 the situation is worse, as can be seen in the bottom part of Fig 9. Most of the evolutionary runs suffered of the bootstrap problem, and present a very flat fitness surface. Only a minority of runs were able to discover a suitable strategy that was optimised through the generations. This also indicates that there exist possible solutions to the proposed problem, but they are not easily obtained by a SOO approach.

Bottom Line: A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled).This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics.In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.

ABSTRACT
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

No MeSH data available.