Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components(1-3). Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable(4). Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules(5,6). Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out(7,8). Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors(9,10), and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks11: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks(12,13). Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures(14-16).