Cerebras has shown off the capabilities of its second–generation wafer–scale engine, announcing it has set the record for the largest AI model ever trained on a single device. For the first time, a natural language processing network with 20 billion parameters, GPT–NeoX 20B, was trained on a single device. A new type of neural network, the transformer, is taking over. Today, transformers are mainly used for natural language processing (NLP) where their attention mechanism can help spot the relationship between words in a sentence, but they are spreading to other AI applications, including vision. The bigger a transformer is, the more accurate it is.

It is a known fact that the performance of optimization algorithms for NP-Hard problems vary from instance to instance. We observed the same trend when we comprehensively studied multi-objective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in a construction project. In this paper, instead of using a single algorithm to solve DTCTP, we use a portfolio approach that takes multiple algorithms as its constituent. We proposed portfolio comprising of four MOEAs, Non-dominated Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the portfolio approach is computationally fast and qualitatively superior to its constituent algorithms for all benchmark instances. Moreover, portfolio approach provides an insight in selecting the best algorithm for all benchmark instances of DTCTP.