Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” continuous and firstly performed by Professors L. Feng H.D. Wang, “4” proposed our group for discrete cases. As computational paradigm, theoretical insights into relatively rare now. Therefore, we p...

Objectives . A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other indices, whose values are thus preset. For scalar objective function, global minimum sought that contains restricted indices as penalty terms. However, landscape such function has steep-ascent areas, which significantly complicate search minimum. T...

This section and the results in the table address the question: What effects do GDSS have on group processes and outcomes? Our initial studies focused on differences between groups using the GDSS and groups employing more traditional modes of operation. Normatively we were interested in the question of whether there was any net improvement in decision outcomes such as quality, satisfaction, and...

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...