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Classical gradient methods and evolutionary algorithms represent two very different classes of optimization techniques that seem to have very different properties. This paper discusses some aspects of some "obvious" differences and explores to what extent a hybrid method, the evolutionary-gradient-search procedure, can be used beneficially in the field of continuous parameter optimization. Simulation experiments show that on some test functions, the hybrid method yields faster convergence than pure evolution strategies, but that on other test functions, the procedure exhibits the same deficiencies as steepest-descent methods.

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Muscle activation differs between the straight and the curves and between legs; ie, average activities of selected muscles of the right leg were significantly higher during skating through the curves than in the straights. This could not be observed for the left leg. The reduction in speed during the 1000-m TT highly correlates with the decrease in the muscle activity of both the tibialis anterior and the rectus femoris of the right leg. Muscle recruitment is different in relation to lap section (straight vs curve) and leg (right vs left leg). The decreased muscle activity of the tibialis anterior and rectus femoris of the right leg showed the highest relationships with the reduction in skating speed during the 1000-m TT.