210,478 research outputs found
Evolutionary approaches to explainable machine learning
Machine learning models are increasingly being used in critical sectors, but
their black-box nature has raised concerns about accountability and trust. The
field of explainable artificial intelligence (XAI) or explainable machine
learning (XML) has emerged in response to the need for human understanding of
these models. Evolutionary computing, as a family of powerful optimization and
learning tools, has significant potential to contribute to XAI/XML. In this
chapter, we provide a brief introduction to XAI/XML and review various
techniques in current use for explaining machine learning models. We then focus
on how evolutionary computing can be used in XAI/XML, and review some
approaches which incorporate EC techniques. We also discuss some open
challenges in XAI/XML and opportunities for future research in this field using
EC. Our aim is to demonstrate that evolutionary computing is well-suited for
addressing current problems in explainability, and to encourage further
exploration of these methods to contribute to the development of more
transparent, trustworthy and accountable machine learning models
Evaluating Stationary Distribution of the Binary GA Markov Chain in Special Cases
The evolutionary algorithm stochastic process is well-known to be
Markovian. These have been under investigation in much of the
theoretical evolutionary computing research. When mutation rate is
positive, the Markov chain modeling an evolutionary algorithm is
irreducible and, therefore, has a unique stationary distribution,
yet, rather little is known about the stationary distribution. On the other
hand, knowing the stationary distribution may provide
some information about the expected times to hit optimum, assessment of the biases due to recombination and is of importance in population
genetics to assess what\u27s called a ``genetic load" (see the
introduction for more details). In this talk I will show how the quotient
construction method can be exploited to derive rather explicit bounds on the ratios of the stationary distribution values of various subsets of
the state space. In fact, some of the bounds obtained in the current
work are expressed in terms of the parameters involved in all the
three main stages of an evolutionary algorithm: namely selection,
recombination and mutation. I will also discuss the newest developments which may allow for further improvements of the bound
USING INSTITUTIONS TO BRIDGE THE TRUST-GAP IN UTILITY COMPUTING MARKETS – AN EXTENDED “TRUST-GAME”
With the ongoing evolution of the Internet as a trading platform and the corresponding paradigm change from small non-anonymous markets to their large anonymous utility computing pendants, new challenges arise. One of them is the promotion of trust in these new markets as it is an essential prerequisite for bilateral economic exchange [2]. This work tries to meet this challenge by using an evolutionary game-theoretic approach in combination with institutions. Starting from a basic trust game it will show that the introduction of institutions will lead to the crowding in of trustworthy behavior, even if no special detection capabilities are available
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
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