  As "a new frontier in evolutionary computation research", evolutionary
transfer optimization(ETO) will overcome the traditional paradigm of zero reuse
of related experience and knowledge from solved past problems in researches of
evolutionary computation. In scheduling applications via ETO, a quite appealing
and highly competitive framework "meeting" between them could be formed for
both intelligent scheduling and green scheduling, especially for international
pledge of "carbon neutrality" from China. To the best of our knowledge, our
paper on scheduling here, serves as the 1st work of a class of ETO frameworks
when multiobjective optimization problem "meets" single-objective optimization
problems in discrete case (not multitasking optimization). More specifically,
key knowledge conveyed for industrial applications, like positional building
blocks with genetic algorithm based settings, could be used via the new core
transfer mechanism and learning techniques for permutation flow shop scheduling
problem(PFSP). Extensive studies on well-studied benchmarks validate firm
effectiveness and great universality of our proposed ETO-PFSP framework
empirically. Our investigations (1) enrich the ETO frameworks, (2) contribute
to the classical and fundamental theory of building block for genetic
algorithms and memetic algorithms, and (3) head towards the paradigm shift of
evolutionary scheduling via learning by proposal and practice of paradigm of
"knowledge and building-block based scheduling" (KAB2S) for "industrial
intelligence" in China.
