A distinction is sometimes made between"statistical"and"subjective"probabilities. This is based on a distinction between"unique"events and"repeatable"events. We argue that this distinction is untenable, since all events are"unique"and all events belong to"kinds", and offer a conception of probability for A1 in which (1) all probabilities are based on -- possibly vague -- statistical knowledge, ...

Many-objective optimization problems (MaOPs) are challenging in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides objective space into projection plane free dimension(s). The balance between conver...

Multi-modal multi-objective optimization problems (MMMOPs) have multiple subsets within the Pareto-optimal Set, each independently mapping to same Pareto-Front. Prevalent evolutionary algorithms are not purely designed search for solution subsets, whereas, MMMOPs demonstrate degraded performance in objective space. This motivates design of better addressing MMMOPs. The present work identifies c...

The common distinction between probabilities that can be based on frequencies known to hold in a sequence of repeatable events, and probabilities that concern unique events, and that therefore must be based on subjective opinion, is argued to be misguided. All events are in some relevant sense "unique", and, more importantly, all events can in a relevant sense be placed in classes of similar ev...

Preservation of local similarity structure is a key challenge in deep clustering. Many recent clustering methods therefore use autoencoders to help guide the model's neural network towards an embedding which more reflective input space geometry. However, work has shown that autoencoder-based models can suffer from objective function mismatch (OFM). In order improve preservation structure, while...