Lexicase selection is an effective many-objective evolutionary algorithm across many problem domains. Lexicase can be computationally expensive, especially in areas like evolutionary robotics where individual objectives might require their own physics simulation. Improving the efficiency of Lexicase selection can reduce the total number of evaluations thereby lowering computational overhead. Here, we introduce a fitness agnostic adaptive objective sampling algorithm using the filtering efficacy of objectives to adjust their frequency of occurrence as a selector. In a set of binary genome maximization tasks modeled to emulate evolutionary robotics situations, we show that performance can be maintained while computational efficiency increases as compared to ϵ -Lexicase.

Controllers capable of exhibiting multiple behaviors is a longstanding goal in artificial life. Evolutionary robotics approaches have demonstrated effective optimization of robotic controllers, realizing single behaviors in a variety of domains. However, evolving multiple behaviors in one controller remains an outstanding challenge. Many objective selection algorithms are a potential solution as they are capable of optimizing across tens or hundreds of objectives. In this study, we use Lexicase selection evolving animats capable of both wall crossing and turn/seek behaviors. Our investigation focuses on the objective sampling strategy during selection to balance performance across the two primary tasks. Results show that the sampling strategy does not significantly alter performance, but the number of evaluations required varies significantly across strategies.

Generalized behavior is a long standing goal for evolutionary robotics. Behaviors for a given task should be robust to perturbation and capable of operating across a variety of environments. We have previously shown that Lexicase selection evolves high-performing individuals in a semi-generalized wall crossing task–i.e., where the task is broadly the same, but there is variation between individual instances. Further work has identified effective parameter values for Lexicase selection in this domain but other factors affecting and explaining performance remain to be identified. In this paper, we expand our prior investigations, examining populations over evolutionary time exploring other factors that might lead to generalized behavior. Results show that genomic clusters do not correspond to performance, indicating that clusters of specialists do not form within the population. While early individuals gain a foothold in the selection process by specializing on a few wall heights, successful populations are ultimately pressured towards generalized behavior. Finally, we find that this transition from specialists to generalists also leads to an increase in tiebreaks, a mechanism in Lexicase, during selection providing a metric to assess the performance of individual replicates.

Agents exhibiting generalized control are capable of solving a theme of related tasks, rather than a specific instance. Here, generalized control pertains to the locomotive capacity of quadrupedal animats, evaluated when climbing over walls of varying height to reach a target. In prior work, we showed that Lexicase selection is more effective than other evolutionary algorithms for this wall crossing task. Generalized controllers capable of crossing the majority of wall heights are discovered, even though Lexicase selection does not sample all possible environments per generation. In this work, we further constrain environmental sampling during evolution, examining the resilience of Lexicase to the impoverished conditions. Through restricting the range of samples at given points in time as well as fixing environmental exposure over fractions of evolutionary time, we attempt to increase the ‘adjacency’ of environmental samples, and report on the response of the Lexicase algorithm to the pressure of this reduced environmental diversity. Results indicate that Lexicase is robust, producing viable agents even in considerably challenging conditions. We also see a positive correlation between the number of tiebreak events that occur and the success of individuals in a population, except in the most limiting conditions. We argue that the increased number of tiebreaks is a response to local maxima, and the increased diversity resulting from random selection at this point, is a key driver of the resilience of the Lexicase algorithm. We also show that in extreme cases, this relationship breaks down. We conclude that tiebreaking is an important control mechanism in Lexicase operation, and that the breakdown in performance observed in extreme conditions indicates an inability of the tiebreak mechanism to function effectively where population diversity is unable to reflect environmental diversity.

A primary goal of evolutionary robotics (ER) is generalized control. That is, a robot controller should be capable of solving a variety of tasks in a domain, rather than only addressing specific instances of a task. Prior work has shown that Lexicase selection is more effective than other evolutionary algorithms for a wall crossing task domain where quadrupedal animats are evaluated on walls of varying height. In this work we expand baseline treatments in this task domain and examine specific aspects of the Lexicase selection algorithm across a variety of different parameter configurations. We identify the most effective Lexicase parameters for this task. Results indicate that Lexicase’s success is potentially due to maintaining population diversity at a higher level than other algorithms explored for this domain.

This paper investigates the hypothesis that noise in the genotype–phenotype mapping, here called stochastic ontogenesis (SO) , is an important consideration in Evolutionary Robotics. This is examined in two ways: first, in the context of seeking to generalise controller performance in an incremental task domain in simulation, and second, in a preliminary study of its effectiveness as a mechanism for crossing the “reality gap” from simulation to physical robots. The performance of evolved neurocontrollers for a fixed-morphology simulated robot is evaluated in both the presence and absence of ontogenic noise, in a task requiring the development of a walking gait that accommodates a varying environment. When SO is applied, evolution of controllers is more effective (replicates achieve higher fitness) and more robust (fewer replicates fail) than evolution using a deterministic mapping. This result is found in a variety of incremental scenarios. For the preliminary study of the utility of SO for moving between simulation and reality, the capacity of evolved controllers to handle unforeseen environmental noise is tested by introducing a stochastic coefficient of friction and evaluating previous populations in the new problem domain. Controllers evolved with deterministic ontogenesis fail to accommodate the new source of noise and show reduced fitness. In contrast, those which experienced ontogenic noise during evolution are not significantly disrupted by the additional noise in the environment. It is argued that SO is a catch-all mechanism for increasing performance of Evolutionary Robotics designs and may have further more general implications for Evolutionary Computation.

Evolving robust behaviors for robots has proven to be a challenging problem. Determining how to optimize behavior for a specific instance, while also realizing behaviors that generalize to variations on the problem often requires highly customized algorithms and problem-specific tuning of the evolutionary platform. Algorithms that can realize robust, generalized behavior without this customization are therefore highly desirable. In this paper, we examine the Lexicase selection algorithm as a possible general algorithm for a wall crossing robot task. Previous work has resulted in specialized strategies to evolve robust behaviors for this task. Here, we show that Lexicase selection is not only competitive with these strategies but after parameter tuning, actually exceeds the performance of the specialized algorithms.

It has been shown that manipulation of objects by 3D virtual creatures can play an important role in the evolution of complex, embodied sensorimotor behaviours. In this work we examine the capacity of virtual creatures that use evolutionary and control architectures already shown to be capable of sensor-differential gradient-following locomotion (tropotaxis) to adapt to solve a physical problem involving the manipulation of 3D objects in their environments. Specifically, the creatures task is to guide a physically-modelled cube through their environments in order to achieve maximum covered distance of the object. Agents were evolved in the manipulation environment from random initial genotypes and from genotypes previously optimised for performance in a different task. Performance was evaluated both before and after evolutionary adaptation. We show that the architecture achieves embodied feedback control in the block movement task. We observed some overlap between the earlier and later environments but also that success in the first environment does not preclude or entail success in the second. We found that species evolving from scratch do no better or worse than those optimised for a different environment, and that sensory feedback is necessary for correct approach and control behaviours in agents, although close control is less dependent on sensory input than distance approach.