64 research outputs found
From evolutionary computation to the evolution of things
Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems
Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms
Conversion rate optimization means designing web interfaces such that more
visitors perform a desired action (such as register or purchase) on the site.
One promising approach, implemented in Sentient Ascend, is to optimize the
design using evolutionary algorithms, evaluating each candidate design online
with actual visitors. Because such evaluations are costly and noisy, several
challenges emerge: How can available visitor traffic be used most efficiently?
How can good solutions be identified most reliably? How can a high conversion
rate be maintained during optimization? This paper proposes a new technique to
address these issues. Traffic is allocated to candidate solutions using a
multi-armed bandit algorithm, using more traffic on those evaluations that are
most useful. In a best-arm identification mode, the best candidate can be
identified reliably at the end of evolution, and in a campaign mode, the
overall conversion rate can be optimized throughout the entire evolution
process. Multi-armed bandit algorithms thus improve performance and reliability
of machine discovery in noisy real-world environments.Comment: The Thirty-First Innovative Applications of Artificial Intelligence
Conferenc
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present a pure-Python open-source library, called PyPop7,
for black-box optimization (BBO). It provides a unified and modular interface
for more than 60 versions and variants of different black-box optimization
algorithms, particularly population-based optimizers, which can be classified
into 12 popular families: Evolution Strategies (ES), Natural Evolution
Strategies (NES), Estimation of Distribution Algorithms (EDA), Cross-Entropy
Method (CEM), Differential Evolution (DE), Particle Swarm Optimizer (PSO),
Cooperative Coevolution (CC), Simulated Annealing (SA), Genetic Algorithms
(GA), Evolutionary Programming (EP), Pattern Search (PS), and Random Search
(RS). It also provides many examples, interesting tutorials, and full-fledged
API documentations. Through this new library, we expect to provide a
well-designed platform for benchmarking of optimizers and promote their
real-world applications, especially for large-scale BBO. Its source code and
documentations are available at
https://github.com/Evolutionary-Intelligence/pypop and
https://pypop.readthedocs.io/en/latest, respectively.Comment: 5 page
Towards replicated algorithms
The main deficiency of the algorithms running on digital computers nowadays
is their inability to change themselves during the execution. In line with
this, the paper introduces the so-called replicated algorithms, inspired by the
concept of developing a human brain. Similar to the human brain, where the
process of thinking is strongly parallel, replicated algorithms, incorporated
into a population, are also capable of replicating themselves and solving
problems in parallel. They operate as a model for mapping the known input to a
known output. In our preliminary study, these algorithms are built as sequences
of arithmetic operators, applied for calculating arithmetic expressions, while
their behavior showed that they can operate in the condition of open-ended
evolution
Evolving robot software and hardware
This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research
Robot life: simulation and participation in the study of evolution and social behavior.
This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton's Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something (life, evolution, sociality) or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze's reading of Plato on the relationship of ideas, copies and simulacra
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have proposed to drive evolutionary algorithms
using machine learning models. Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models. Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality. To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs). At each generation of the proposed algorithm, the parent
solutions are first classified into real and fake samples to train the GANs;
then the offspring solutions are sampled by the trained GANs. Thanks to the
powerful generative ability of the GANs, our proposed algorithm is capable of
generating promising offspring solutions in high-dimensional decision space
with limited training data. The proposed algorithm is tested on 10 benchmark
problems with up to 200 decision variables. Experimental results on these test
problems demonstrate the effectiveness of the proposed algorithm
Genetic Algorithms ::an Evolutionary Approach to Optical Engineering
We present a Genetic Algorithm that we developed to address optimization problems in optical engineering. Our objective is to determine the global optimum of a problem ideally by a single run of the genetic algorithm. We want also to achieve this objective with a reasonable use of computational resources. In order to accelerate the convergence of the algorithm, we establish generation after generation a quadratic approximation of the fitness in the close neighborhood of the best-so-far individual. We then inject in the population an individual that corresponds to the optimum of this approximation. We also use randomly-shifted Gray codes when applying mutations in order to achieve a better exploration of the parameter space. We provide automatic settings for the technical parameters of our algorithm and apply it to typical benchmark problems in 5, 10 and 20 dimensions. We show that the global optimum of these problems can be determined with a probability of success in one run of the order of 95-97 % and an average number of fitness evaluations of the order of 400-750Ă—n, where n refers to the number of parameters to determine. We finally comment some applications of this algorithm to real-world engineering problems
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