Title: Towards Evolutionary Multitasking: A New Paradigm in Evolutionary Computation

Abstract

The design of population-based search algorithms of evolutionary computation (EC) has traditionally been focused on efficiently solving a single optimization task at a time. It is only very recently that a new paradigm in EC, namely, multifactorial optimization (MFO), has been introduced to explore the potential of evolutionary multitasking (Gupta, Ong, & Feng, 2015). The nomenclature signifies a multitasking search involving multiple optimization tasks at once, with each task contributing a unique factor influencing the evolution of a single population of individuals. MFO is found to leverage the scope for implicit genetic transfer offered by the population in a simple and elegant manner, thereby opening doors to a plethora of new research opportunities in EC, dealing, in particular, with the exploitation of underlying synergies between seemingly unrelated tasks. A strong practical motivation for the paradigm is derived from the rapidly expanding popularity of cloud computing (CC) services. It is noted that CC characteristically provides an environment in which multiple jobs can be received from multiple users at the same time. Thus, assuming each job to correspond to some kind of optimization task, as may be the case in a cloud-based on-demand optimization service, the CC environment is expected to lend itself nicely to the unique features of MFO.

In this talk, the formalization of the concept of MFO is first introduced. A fitness landscape-based approach towards understanding what is truly meant by there being underlying synergies (or what we term as genetic complementarities) between optimization tasks is then discussed. Accordingly, a synergy metric capable of quantifying the complementarity, which shall later be shown to act as a "qualitative" predictor of the success of multitasking is also presented (Gupta, Ong, Da, Feng, and Handoko, 2015). With the above in mind, a novel evolutionary algorithm (EA) for MFO is proposed, one that is inspired by bio-cultural models of multifactorial inheritance, so as to best harness the genetic complementarity between tasks. The salient feature of the algorithm is that it incorporates a unified solution representation scheme which, to a large extent, unites the fields of continuous and discrete optimization. The efficacy of the proposed algorithm, and the concept of MFO in general, shall finally be substantiated via a variety of computation experiments in intra and inter-domain evolutionary multitasking.

Title: Singularity of Future Computer-System Networks

Abstract

As a supercomputer becomes large, over million cores using over million Watts, its network design is more complex. In this talk, I explain its network requirements and trends, especially network topology for low communication latency, and low power consumption. I then recommend two key unique technologies, random network topologies and free-space optics for making future supercomputers and datacenters.

Title: The Future of Programming is Functional

Abstract

More and more mainstream programming languages are adopting concepts from Functional Programming. Good examples are the recent support for lambda expressions, streams and the Optional type in Java 8, the lambda functions in C++13 and the LINQ framework in C#. This talk traces these features back to their source in the Functional Programming paradigm and considers what Functional Programming has in store for the future of mainstream programming. In particular we consider the explicit and flexible control of side-effects in the form of monads (aka the overloadable semi-colon). Monads now come in a new and more palatable form: (algebraic) effect handlers. Effect handlers generalise the well-known exception handlers to support a wide-range of user-defined side-effects that can be easily combined.