The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves
about the idea of exploiting the problem structure to implement more efficient evolutionary …

With the further and faster urbanization, here come the advent and development of
intelligent public transportation system. The identification of the traffic signs, as a key …

When searching for input configurations that optimise the output of a system, it can be useful
to build a statistical model of the system being optimised. This is done in approaches such …

We present a new Estimation of Distribution algorithms (EDA) based on two novel
Variational Autoencoders generative model building algorithms. The first method …

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be
efficiently learned and sampled. Generative Adversarial Networks (GAN) are generative …

Among the characteristics of traditional evolutionary algorithms governed by models,
memory volatility is one of the most frequent. This is commonly due to the limitations of the …

Many systems take inputs, which can be measured and sometimes controlled, and outputs,
which can also be measured and which depend on the inputs. Taking numerous …

Evolutionary algorithms are a class of optimisation techniques used to solve problems by
emulating evolutionary processes (variation and selection) to search the solution space. In …

As the name indicates, single-solution search-based algorithms organize the search by
maintaining a single solution at each step. The transitions between solutions can be done …

Mixed order hyper networks (MOHNs)[7] are neural network models capable of representing
any function in f: 1-1, 1ln→ R. They are linear parameter models, which means there are …