In this position paper, we consider some foundational topics regarding smart contracts (such as terminology, automation, enforceability, and semantics) and define a smart contract as an automatable and enforceable agreement. We explore a simple semantic framework for smart contracts, covering both operational and non-operational aspects, and describe templates and agreements for legally-enforceable smart contracts, based on legal documents. Building upon the Ricardian Contract, we identify operational parameters in the legal documents and use these to connect legal agreements to standardised code. We also explore the design landscape, including increasing sophistication of parameters, increasing use of common standardised code, and long-term research.

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Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: the crossover operator. The motivation for this study is to provide an adaptive crossover operator that gives best overall performance on a large set of problems. A new adaptive crossover operator "selective crossover" is proposed and is compared with two-point and uniform crossover on a problem generator where epistasis can be varied and on trap functions where deception can be varied. We provide empirical results which show that selective crossover is more efficient than two-point and uniform crossover across a representative set of search problems containing epistasis.